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129 results (View BibTeX file of all listed publications)

2007

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Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism

Saigo, H., Hattori, M., Tsuda, K.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
Secondary metabolic pathway in plant is important for finding druggable candidate enzymes. However, there are many enzymes whose functions are still undiscovered especially in organism-specific metabolic pathways. We propose reaction graph kernels for automatically assigning the EC numbers to unknown enzymatic reactions in a metabolic network. Experiments are carried out on KEGG/REACTION database and our method successfully predicted the first three digits of the EC number with 83% accuracy.We also exhaustively predicted missing enzymatic functions in the plant secondary metabolism pathways, and evaluated our results in biochemical validity.

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2007

Saigo, H., Hattori, M., Tsuda, K. Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Positional Oligomer Importance Matrices

Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences, above all of DNA and proteins. In many cases, the most accurate classifiers are obtained by training SVMs with complex sequence kernels, for instance for transcription starts or splice sites. However, an often criticized downside of SVMs with complex kernels is that it is very hard for humans to understand the learned decision rules and to derive biological insights from them. To close this gap, we introduce the concept of positional oligomer importance matrices (POIMs) and develop an efficient algorithm for their computation. We demonstrate how they overcome the limitations of sequence logos, and how they can be used to find relevant motifs for different biological phenomena in a straight-forward way. Note that the concept of POIMs is not limited to interpreting SVMs, but is applicable to general k−mer based scoring systems.

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Sonnenburg, S., Zien, A., Philips, P., Rätsch, G. Positional Oligomer Importance Matrices NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Machine Learning Algorithms for Polymorphism Detection

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G. Machine Learning Algorithms for Polymorphism Detection NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Zien, A., Ong, C.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions.We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We utilize an extension of the multiclass support vector machine (SVM)method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets, and show that we perform better than the current state of the art. Furthermore, our method provides some insights as to which features are most useful for determining subcellular localization, which are in agreement with biological reasoning.

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Zien, A., Ong, C. An Automated Combination of Kernels for Predicting Protein Subcellular Localization NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration

Hill, NJ.

Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

Abstract
I‘ll present a perspective on Brain-Computer Interface development from T{\"u}bingen. Some of the benefits promised by BCI technology lie in the near foreseeable future, and some further away. Our motivation is to make BCI technology feasible for the people who could benefit from what it has to offer soon: namely, people in the "completely locked-in" state. I‘ll mention some of the challenges of working with this user group, and explain the specific directions they have motivated us to take in developing experimental methods, algorithms, and software.

[BibTex]

Hill, NJ. Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

[BibTex]

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Towards compliant humanoids: an experimental assessment of suitable task space position/orientation controllers

Nakanishi, J., Mistry, M., Peters, J., Schaal, S.

In IROS 2007, 2007, pages: 2520-2527, (Editors: Grant, E. , T. C. Henderson), IEEE Service Center, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2007 (inproceedings)

Abstract
Compliant control will be a prerequisite for humanoid robotics if these robots are supposed to work safely and robustly in human and/or dynamic environments. One view of compliant control is that a robot should control a minimal number of degrees-of-freedom (DOFs) directly, i.e., those relevant DOFs for the task, and keep the remaining DOFs maximally compliant, usually in the null space of the task. This view naturally leads to task space control. However, surprisingly few implementations of task space control can be found in actual humanoid robots. This paper makes a first step towards assessing the usefulness of task space controllers for humanoids by investigating which choices of controllers are available and what inherent control characteristics they have—this treatment will concern position and orientation control, where the latter is based on a quaternion formulation. Empirical evaluations on an anthropomorphic Sarcos master arm illustrate the robustness of the different controllers as well as the eas e of implementing and tuning them. Our extensive empirical results demonstrate that simpler task space controllers, e.g., classical resolved motion rate control or resolved acceleration control can be quite advantageous in face of inevitable modeling errors in model-based control, and that well chosen formulations are easy to implement and quite robust, such that they are useful for humanoids.

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Nakanishi, J., Mistry, M., Peters, J., Schaal, S. Towards compliant humanoids: an experimental assessment of suitable task space position/orientation controllers In IROS 2007, 2007, pages: 2520-2527, (Editors: Grant, E. , T. C. Henderson), IEEE Service Center, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2007 (inproceedings)

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Sistema avanzato per la classificazione delle aree agricole in immagini ad elevata risoluzione geometrica: applicazione al territorio del Trentino

Arnoldi, E., Bruzzone, L., Carlin, L., Pedron, L., Persello, C.

In pages: 1-6, 11. Conferenza Nazionale ASITA, November 2007 (inproceedings)

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Arnoldi, E., Bruzzone, L., Carlin, L., Pedron, L., Persello, C. Sistema avanzato per la classificazione delle aree agricole in immagini ad elevata risoluzione geometrica: applicazione al territorio del Trentino In pages: 1-6, 11. Conferenza Nazionale ASITA, November 2007 (inproceedings)

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Performance Stabilization and Improvement in Graph-based Semi-supervised Learning with Ensemble Method and Graph Sharpening

Choi, I., Shin, H.

In Korean Data Mining Society Conference, pages: 257-262, Korean Data Mining Society, Seoul, Korea, Korean Data Mining Society Conference, November 2007 (inproceedings)

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Choi, I., Shin, H. Performance Stabilization and Improvement in Graph-based Semi-supervised Learning with Ensemble Method and Graph Sharpening In Korean Data Mining Society Conference, pages: 257-262, Korean Data Mining Society, Seoul, Korea, Korean Data Mining Society Conference, November 2007 (inproceedings)

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Policy Learning for Robotics

Peters, J.

14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

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Peters, J. Policy Learning for Robotics 14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

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Hilbert Space Representations of Probability Distributions

Gretton, A.

2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

Abstract
Many problems in unsupervised learning require the analysis of features of probability distributions. At the most fundamental level, we might wish to determine whether two distributions are the same, based on samples from each - this is known as the two-sample or homogeneity problem. We use kernel methods to address this problem, by mapping probability distributions to elements in a reproducing kernel Hilbert space (RKHS). Given a sufficiently rich RKHS, these representations are unique: thus comparing feature space representations allows us to compare distributions without ambiguity. Applications include testing whether cancer subtypes are distinguishable on the basis of DNA microarray data, and whether low frequency oscillations measured at an electrode in the cortex have a different distribution during a neural spike. A more difficult problem is to discover whether two random variables drawn from a joint distribution are independent. It turns out that any dependence between pairs of random variables can be encoded in a cross-covariance operator between appropriate RKHS representations of the variables, and we may test independence by looking at a norm of the operator. We demonstrate this independence test by establishing dependence between an English text and its French translation, as opposed to French text on the same topic but otherwise unrelated. Finally, we show that this operator norm is itself a difference in feature means.

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Gretton, A. Hilbert Space Representations of Probability Distributions 2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

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Discriminative Subsequence Mining for Action Classification

Nowozin, S., BakIr, G., Tsuda, K.

In ICCV 2007, pages: 1919-1923, IEEE Computer Society, Los Alamitos, CA, USA, 11th IEEE International Conference on Computer Vision, October 2007 (inproceedings)

Abstract
Recent approaches to action classification in videos have used sparse spatio-temporal words encoding local appearance around interesting movements. Most of these approaches use a histogram representation, discarding the temporal order among features. But this ordering information can contain important information about the action itself, e.g. consider the sport disciplines of hurdle race and long jump, where the global temporal order of motions (running, jumping) is important to discriminate between the two. In this work we propose to use a sequential representation which retains this temporal order. Further, we introduce Discriminative Subsequence Mining to find optimal discriminative subsequence patterns. In combination with the LPBoost classifier, this amounts to simultaneously learning a classification function and performing feature selection in the space of all possible feature sequences. The resulting classifier linearly combines a small number of interpretable decision functions, each checking for the presence of a single discriminative pattern. The classifier is benchmarked on the KTH action classification data set and outperforms the best known results in the literature.

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Nowozin, S., BakIr, G., Tsuda, K. Discriminative Subsequence Mining for Action Classification In ICCV 2007, pages: 1919-1923, IEEE Computer Society, Los Alamitos, CA, USA, 11th IEEE International Conference on Computer Vision, October 2007 (inproceedings)

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Regression with Intervals

Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A.

International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

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Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A. Regression with Intervals International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

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Unsupervised Classification for non-invasive Brain-Computer-Interfaces

Eren, S., Grosse-Wentrup, M., Buss, M.

In Automed 2007, pages: 65-66, VDI Verlag, Düsseldorf, Germany, Automed Workshop, October 2007 (inproceedings)

Abstract
Non-invasive Brain-Computer-Interfaces (BCIs) are devices that infer the intention of human subjects from signals generated by the central nervous system and recorded outside the skull, e.g., by electroencephalography (EEG). They can be used to enable basic communication for patients who are not able to communicate by normal means, e.g., due to neuro-degenerative diseases such as amyotrophic lateral sclerosis (ALS) (see [Vaughan2003] for a review). One challenge in research on BCIs is minimizing the training time prior to usage of the BCI. Since EEG patterns vary across subjects, it is usually necessary to record a number of trials in which the intention of the user is known to train a classifier. This classifier is subsequently used to infer the intention of the BCI-user. In this paper, we present the application of an unsupervised classification method to a binary noninvasive BCI based on motor imagery. The result is a BCI that does not require any training, since the mapping from EEG pattern changes to the intention of the user is learned online by the BCI without any feedback. We present experimental results from six healthy subjects, three of which display classification errors below 15%. We conclude that unsupervised BCIs are a viable option, but not yet as reliable as supervised BCIs. The rest of this paper is organized as follows. In the Methods section, we first introduce the experimental paradigm. This is followed by a description of the methods used for spatial filtering, feature extraction, and unsupervised classification. We then present the experimental results, and conclude the paper with a brief discussion.

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Eren, S., Grosse-Wentrup, M., Buss, M. Unsupervised Classification for non-invasive Brain-Computer-Interfaces In Automed 2007, pages: 65-66, VDI Verlag, Düsseldorf, Germany, Automed Workshop, October 2007 (inproceedings)

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A Hilbert Space Embedding for Distributions

Smola, A., Gretton, A., Song, L., Schölkopf, B.

In Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 , pages: 13-31, (Editors: M Hutter and RA Servedio and E Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory (ALT), October 2007 (inproceedings)

Abstract
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.

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Smola, A., Gretton, A., Song, L., Schölkopf, B. A Hilbert Space Embedding for Distributions In Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 , pages: 13-31, (Editors: M Hutter and RA Servedio and E Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory (ALT), October 2007 (inproceedings)

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Cluster Identification in Nearest-Neighbor Graphs

Maier, M., Hein, M., von Luxburg, U.

In ALT 2007, pages: 196-210, (Editors: Hutter, M. , R. A. Servedio, E. Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory, October 2007 (inproceedings)

Abstract
Assume we are given a sample of points from some underlying distribution which contains several distinct clusters. Our goal is to construct a neighborhood graph on the sample points such that clusters are ``identified‘‘: that is, the subgraph induced by points from the same cluster is connected, while subgraphs corresponding to different clusters are not connected to each other. We derive bounds on the probability that cluster identification is successful, and use them to predict ``optimal‘‘ values of k for the mutual and symmetric k-nearest-neighbor graphs. We point out different properties of the mutual and symmetric nearest-neighbor graphs related to the cluster identification problem.

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Maier, M., Hein, M., von Luxburg, U. Cluster Identification in Nearest-Neighbor Graphs In ALT 2007, pages: 196-210, (Editors: Hutter, M. , R. A. Servedio, E. Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory, October 2007 (inproceedings)

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Support Vector Machine Learning for Interdependent and Structured Output Spaces

Altun, Y., Hofmann, T., Tsochantaridis, I.

In Predicting Structured Data, pages: 85-104, Advances in neural information processing systems, (Editors: Bakir, G. H. , T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V. N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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Altun, Y., Hofmann, T., Tsochantaridis, I. Support Vector Machine Learning for Interdependent and Structured Output Spaces In Predicting Structured Data, pages: 85-104, Advances in neural information processing systems, (Editors: Bakir, G. H. , T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V. N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Web [BibTex]

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Inducing Metric Violations in Human Similarity Judgements

Laub, J., Macke, J., Müller, K., Wichmann, F.

In Advances in Neural Information Processing Systems 19, pages: 777-784, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Attempting to model human categorization and similarity judgements is both a very interesting but also an exceedingly difficult challenge. Some of the difficulty arises because of conflicting evidence whether human categorization and similarity judgements should or should not be modelled as to operate on a mental representation that is essentially metric. Intuitively, this has a strong appeal as it would allow (dis)similarity to be represented geometrically as distance in some internal space. Here we show how a single stimulus, carefully constructed in a psychophysical experiment, introduces l2 violations in what used to be an internal similarity space that could be adequately modelled as Euclidean. We term this one influential data point a conflictual judgement. We present an algorithm of how to analyse such data and how to identify the crucial point. Thus there may not be a strict dichotomy between either a metric or a non-metric internal space but rather degrees to which potentially large subsets of stimuli are represented metrically with a small subset causing a global violation of metricity.

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Laub, J., Macke, J., Müller, K., Wichmann, F. Inducing Metric Violations in Human Similarity Judgements In Advances in Neural Information Processing Systems 19, pages: 777-784, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

Seeger, M.

In Advances in Neural Information Processing Systems 19, pages: 1233-1240, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical class structure, achieving state-of-the-art results in an order of magnitude less time than previous work.

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Seeger, M. Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods In Advances in Neural Information Processing Systems 19, pages: 1233-1240, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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A Local Learning Approach for Clustering

Wu, M., Schölkopf, B.

In Advances in Neural Information Processing Systems 19, pages: 1529-1536, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.

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Wu, M., Schölkopf, B. A Local Learning Approach for Clustering In Advances in Neural Information Processing Systems 19, pages: 1529-1536, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Brisk Kernel ICA

Jegelka, S., Gretton, A.

In Large Scale Kernel Machines, pages: 225-250, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Abstract
Recent approaches to independent component analysis have used kernel independence measures to obtain very good performance in ICA, particularly in areas where classical methods experience difficulty (for instance, sources with near-zero kurtosis). In this chapter, we compare two efficient extensions of these methods for large-scale problems: random subsampling of entries in the Gram matrices used in defining the independence measures, and incomplete Cholesky decomposition of these matrices. We derive closed-form, efficiently computable approximations for the gradients of these measures, and compare their performance on ICA using both artificial and music data. We show that kernel ICA can scale up to much larger problems than yet attempted, and that incomplete Cholesky decomposition performs better than random sampling.

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Jegelka, S., Gretton, A. Brisk Kernel ICA In Large Scale Kernel Machines, pages: 225-250, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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MR-Based PET Attenuation Correction: Method and Validation

Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B.

Joint Molecular Imaging Conference, September 2007 (talk)

Abstract
PET/MR combines the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET). For quantitative PET information, correction of tissue photon attenuation is mandatory. Usually in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating source, or from the CT scan in case of combined PET/CT. In the case of a PET/MR scanner, there is insufficient space for the rotating source and ideally one would want to calculate the attenuation map from the MR image instead. Since MR images provide information about proton density of the different tissue types, it is not trivial to use this data for PET attenuation correction. We present a method for predicting the PET attenuation map from a given the MR image, using a combination of atlas-registration and recognition of local patterns. Using "leave one out cross validation" we show on a database of 16 MR-CT image pairs that our method reliably allows estimating the CT image from the MR image. Subsequently, as in PET/CT, the PET attenuation map can be predicted from the CT image. On an additional dataset of MR/CT/PET triplets we quantitatively validate that our approach allows PET quantification with an error that is smaller than what would be clinically significant. We demonstrate our approach on T1-weighted human brain scans. However, the presented methods are more general and current research focuses on applying the established methods to human whole body PET/MRI applications.

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Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B. MR-Based PET Attenuation Correction: Method and Validation Joint Molecular Imaging Conference, September 2007 (talk)

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Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces

Grosse-Wentrup, M., Gramann, K., Buss, M.

In Advances in Neural Information Processing Systems 19, pages: 537-544, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
The performance of EEG-based Brain-Computer-Interfaces (BCIs) critically depends on the extraction of features from the EEG carrying information relevant for the classification of different mental states. For BCIs employing imaginary movements of different limbs, the method of Common Spatial Patterns (CSP) has been shown to achieve excellent classification results. The CSP-algorithm however suffers from a lack of robustness, requiring training data without artifacts for good performance. To overcome this lack of robustness, we propose an adaptive spatial filter that replaces the training data in the CSP approach by a-priori information. More specifically, we design an adaptive spatial filter that maximizes the ratio of the variance of the electric field originating in a predefined region of interest (ROI) and the overall variance of the measured EEG. Since it is known that the component of the EEG used for discriminating imaginary movements originates in the motor cortex, we design two adaptive spatial filters with the ROIs centered in the hand areas of the left and right motor cortex. We then use these to classify EEG data recorded during imaginary movements of the right and left hand of three subjects, and show that the adaptive spatial filters outperform the CSP-algorithm, enabling classification rates of up to 94.7 % without artifact rejection.

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Grosse-Wentrup, M., Gramann, K., Buss, M. Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces In Advances in Neural Information Processing Systems 19, pages: 537-544, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Branch and Bound for Semi-Supervised Support Vector Machines

Chapelle, O., Sindhwani, V., Keerthi, S.

In Advances in Neural Information Processing Systems 19, pages: 217-224, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Semi-supervised SVMs (S3VMs) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the full potential of S3VMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, globally optimal solutions. Empirical evidence suggests that the globally optimal solution can return excellent generalization performance in situations where other implementations fail completely. While our current implementation is only applicable to small datasets, we discuss variants that can potentially lead to practically useful algorithms.

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Chapelle, O., Sindhwani, V., Keerthi, S. Branch and Bound for Semi-Supervised Support Vector Machines In Advances in Neural Information Processing Systems 19, pages: 217-224, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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A Kernel Method for the Two-Sample-Problem

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.

In Advances in Neural Information Processing Systems 19, pages: 513-520, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be computed in $O(m^2)$ time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

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Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A. A Kernel Method for the Two-Sample-Problem In Advances in Neural Information Processing Systems 19, pages: 513-520, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models

Keerthi, S., Sindhwani, V., Chapelle, O.

In Advances in Neural Information Processing Systems 19, pages: 673-680, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold cross-validation error, using non-linear optimization techniques. The key computation in this approach is that of the gradient of the validation function with respect to hyperparameters. We show that for large-scale problems involving a wide choice of kernel-based models and validation functions, this computation can be very efficiently done; often within just a fraction of the training time. Empirical results show that a near-optimal set of hyperparameters can be identified by our approach with very few training rounds and gradient computations.

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Keerthi, S., Sindhwani, V., Chapelle, O. An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models In Advances in Neural Information Processing Systems 19, pages: 673-680, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Learning Dense 3D Correspondence

Steinke, F., Schölkopf, B., Blanz, V.

In Advances in Neural Information Processing Systems 19, pages: 1313-1320, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Establishing correspondence between distinct objects is an important and nontrivial task: correctness of the correspondence hinges on properties which are difficult to capture in an a priori criterion. While previous work has used a priori criteria which in some cases led to very good results, the present paper explores whether it is possible to learn a combination of features that, for a given training set of aligned human heads, characterizes the notion of correct correspondence. By optimizing this criterion, we are then able to compute correspondence and morphs for novel heads.

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Steinke, F., Schölkopf, B., Blanz, V. Learning Dense 3D Correspondence In Advances in Neural Information Processing Systems 19, pages: 1313-1320, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space

Ulges, A., Lampert, CH., Keysers, D., Breuel, TM.

In DAGM 2007, pages: 204-215, (Editors: Hamprecht, F. A., C. Schnörr, B. Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition, September 2007 (inproceedings)

Abstract
The extraction of a parametric global motion from a motion field is a task with several applications in video processing. We present two probabilistic formulations of the problem and carry out optimization using the RAST algorithm, a geometric matching method novel to motion estimation in video. RAST uses an exhaustive and adaptive search of transformation space and thus gives -- in contrast to local sampling optimization techniques used in the past -- a globally optimal solution. Among other applications, our framework can thus be used as a source of ground truth for benchmarking motion estimation algorithms. Our main contributions are: first, the novel combination of a state-of- the-art MAP criterion for dominant motion estimation with a search procedure that guarantees global optimality. Second, experimental re- sults that illustrate the superior performance of our approach on synthetic flow fields as well as real-world video streams. Third, a significant speedup of the search achieved by extending the mod el with an additional smoothness prior.

PDF Web DOI [BibTex]

Ulges, A., Lampert, CH., Keysers, D., Breuel, TM. Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space In DAGM 2007, pages: 204-215, (Editors: Hamprecht, F. A., C. Schnörr, B. Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition, September 2007 (inproceedings)

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Training a Support Vector Machine in the Primal

Chapelle, O.

In Large Scale Kernel Machines, pages: 29-50, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007, This is a slightly updated version of the Neural Computation paper (inbook)

Abstract
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and that there is no reason to ignore this possibility. On the contrary, from the primal point of view new families of algorithms for large scale SVM training can be investigated.

PDF Web [BibTex]

Chapelle, O. Training a Support Vector Machine in the Primal In Large Scale Kernel Machines, pages: 29-50, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007, This is a slightly updated version of the Neural Computation paper (inbook)

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Approximation Methods for Gaussian Process Regression

Quiñonero-Candela, J., Rasmussen, CE., Williams, CKI.

In Large-Scale Kernel Machines, pages: 203-223, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Abstract
A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.

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Quiñonero-Candela, J., Rasmussen, CE., Williams, CKI. Approximation Methods for Gaussian Process Regression In Large-Scale Kernel Machines, pages: 203-223, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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Solving Deep Memory POMDPs with Recurrent Policy Gradients

Wierstra, D., Förster, A., Peters, J., Schmidhuber, J.

In ICANN‘07, pages: 697-706, Springer, Berlin, Germany, International Conference on Artificial Neural Networks, September 2007 (inproceedings)

Abstract
This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov decision problems (POMDPs) that require long-term memories of past observations. The approach involves approximating a policy gradient for a Recurrent Neural Network (RNN) by backpropagating return-weighted characteristic eligibilities through time. Using a “Long Short-Term Memory” architecture, we are able to outperform other RL methods on two important benchmark tasks. Furthermore, we show promising results on a complex car driving simulation task.

PDF PDF DOI [BibTex]

Wierstra, D., Förster, A., Peters, J., Schmidhuber, J. Solving Deep Memory POMDPs with Recurrent Policy Gradients In ICANN‘07, pages: 697-706, Springer, Berlin, Germany, International Conference on Artificial Neural Networks, September 2007 (inproceedings)

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Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces

Altun, Y., Smola, A.

In Predicting Structured Data, pages: 283-300, Advances in neural information processing systems, (Editors: BakIr, G. H., T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V.N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Abstract
In this paper we study the problem of estimating conditional probability distributions for structured output prediction tasks in Reproducing Kernel Hilbert Spaces. More specically, we prove decomposition results for undirected graphical models, give constructions for kernels, and show connections to Gaussian Process classi- cation. Finally we present ecient means of solving the optimization problem and apply this to label sequence learning. Experiments on named entity recognition and pitch accent prediction tasks demonstrate the competitiveness of our approach.

Web [BibTex]

Altun, Y., Smola, A. Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces In Predicting Structured Data, pages: 283-300, Advances in neural information processing systems, (Editors: BakIr, G. H., T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V.N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Web [BibTex]

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Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models

Chiappa, S., Barber, D.

In ISPA 2007, pages: 446-451, IEEE Computer Society, Los Alamitos, CA, USA, 5th International Symposium on Image and Signal Processing and Analysis, September 2007 (inproceedings)

Abstract
We consider a model to cluster the components of a vector time-series. The task is to assign each component of the vector time-series to a single cluster, basing this assignment on the simultaneous dynamical similarity of the component to other components in the cluster. This is in contrast to the more familiar task of clustering a set of time-series based on global measures of their similarity. The model is based on a Dirichlet Mixture of Linear Gaussian State-Space models (LGSSMs), in which each LGSSM is treated with a prior to encourage the simplest explanation. The resulting model is approximated using a ‘collapsed’ variational Bayes implementation.

PDF Web DOI [BibTex]

Chiappa, S., Barber, D. Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models In ISPA 2007, pages: 446-451, IEEE Computer Society, Los Alamitos, CA, USA, 5th International Symposium on Image and Signal Processing and Analysis, September 2007 (inproceedings)

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Manifold Denoising

Hein, M., Maier, M.

In Advances in Neural Information Processing Systems 19, pages: 561-568, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We consider the problem of denoising a noisily sampled submanifold $M$ in $R^d$, where the submanifold $M$ is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample. We analyze this diffusion process using recent results about the convergence of graph Laplacians. In the experiments we show that our method is capable of dealing with non-trivial high-dimensional noise. Moreover using the denoising algorithm as pre-processing method we can improve the results of a semi-supervised learning algorithm.

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Hein, M., Maier, M. Manifold Denoising In Advances in Neural Information Processing Systems 19, pages: 561-568, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements

Kienzle, W., Schölkopf, B., Wichmann, F., Franz, M.

In Pattern Recognition, pages: 405-414, (Editors: FA Hamprecht and C Schnörr and B Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition (DAGM), September 2007 (inproceedings)

Abstract
Interest point detection in still images is a well-studied topic in computer vision. In the spatiotemporal domain, however, it is still unclear which features indicate useful interest points. In this paper we approach the problem by emph{learning} a detector from examples: we record eye movements of human subjects watching video sequences and train a neural network to predict which locations are likely to become eye movement targets. We show that our detector outperforms current spatiotemporal interest point architectures on a standard classification dataset.

PDF Web DOI [BibTex]

Kienzle, W., Schölkopf, B., Wichmann, F., Franz, M. How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements In Pattern Recognition, pages: 405-414, (Editors: FA Hamprecht and C Schnörr and B Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition (DAGM), September 2007 (inproceedings)

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Bayesian Inference for Sparse Generalized Linear Models

Seeger, M., Gerwinn, S., Bethge, M.

In ECML 2007, pages: 298-309, Lecture Notes in Computer Science ; 4701, (Editors: Kok, J. N., J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron), Springer, Berlin, Germany, 18th European Conference on Machine Learning, September 2007 (inproceedings)

Abstract
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties such as sparsity or non-negativity. The central role of posterior log-concavity in Bayesian GLMs is emphasized and related to stability issues in EP. In particular, we use our technique to infer the parameters of a point process model for neuronal spiking data from multiple electrodes, demonstrating significantly superior predictive performance when a sparsity assumption is enforced via a Laplace prior distribution.

PDF DOI [BibTex]

Seeger, M., Gerwinn, S., Bethge, M. Bayesian Inference for Sparse Generalized Linear Models In ECML 2007, pages: 298-309, Lecture Notes in Computer Science ; 4701, (Editors: Kok, J. N., J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron), Springer, Berlin, Germany, 18th European Conference on Machine Learning, September 2007 (inproceedings)

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Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions

Walder, C., Schölkopf, B., Chapelle, O.

In Advances in Neural Information Processing Systems 19, pages: 273-280, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We consider the problem of constructing a function whose zero set is to represent a surface, given sample points with surface normal vectors. The contributions include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable properties previously only associated with fully supported bases, and show equivalence to a Gaussian process with modified covariance function. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data.

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Walder, C., Schölkopf, B., Chapelle, O. Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions In Advances in Neural Information Processing Systems 19, pages: 273-280, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Trading Convexity for Scalability

Collobert, R., Sinz, F., Weston, J., Bottou, L.

In Large Scale Kernel Machines, pages: 275-300, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Abstract
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how nonconvexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs.

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Collobert, R., Sinz, F., Weston, J., Bottou, L. Trading Convexity for Scalability In Large Scale Kernel Machines, pages: 275-300, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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Bayesian methods for NMR structure determination

Habeck, M.

29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

Web [BibTex]

Habeck, M. Bayesian methods for NMR structure determination 29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

Web [BibTex]

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Comparison of Adaptive Spatial Filters with Heuristic and Optimized Region of Interest for EEG Based Brain-Computer-Interfaces

Liefhold, C., Grosse-Wentrup, M., Gramann, K., Buss, M.

In Pattern Recognition, pages: 274-283, (Editors: Hamprecht, F. A., C. Schnörr, B. Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition, September 2007 (inproceedings)

Abstract
Research on EEG based brain-computer-interfaces (BCIs) aims at steering devices by thought. Even for simple applications, BCIs require an extremely effective data processing to work properly because of the low signal-to-noise-ratio (SNR) of EEG signals. Spatial filtering is one successful preprocessing method, which extracts EEG components carrying the most relevant information. Unlike spatial filtering with Common Spatial Patterns (CSP), Adaptive Spatial Filtering (ASF) can be adapted to freely selectable regions of interest (ROI) and with this, artifacts can be actively suppressed. In this context, we compare the performance of ASF with ROIs selected using anatomical a-priori information and ASF with numerically optimized ROIs. Therefore, we introduce a method for data driven spatial filter adaptation and apply the achieved filters for classification of EEG data recorded during imaginary movements of the left and right hand of four subjects. The results show, that in the case of artifact-free datasets, ASFs with numerically optimized ROIs achieve classification rates of up to 97.7 % while ASFs with ROIs defined by anatomical heuristic stay at 93.7 % for the same data. Otherwise, with noisy datasets, the former brake down (66.7 %) while the latter meet 95.7 %.

PDF Web DOI [BibTex]

Liefhold, C., Grosse-Wentrup, M., Gramann, K., Buss, M. Comparison of Adaptive Spatial Filters with Heuristic and Optimized Region of Interest for EEG Based Brain-Computer-Interfaces In Pattern Recognition, pages: 274-283, (Editors: Hamprecht, F. A., C. Schnörr, B. Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition, September 2007 (inproceedings)

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Machine Learning and Applications in Biology

Shin, H.

In BioKorea 2007, pages: 337-366, BioKorea, September 2007 (inproceedings)

Abstract
The emergence of the fields of computational biology and bioinformatics has alleviated the burden of solving many biological problems, saving the time and cost required for experiments and also providing predictions that guide new experiments. Within computational biology, machine learning algorithms have played a central role in dealing with the flood of biological data. The goal of this tutorial is to raise awareness and comprehension of machine learning so that biologists can properly match the task at hand to the corresponding analytical approach. We start by categorizing biological problem settings and introduce the general machine learning schemes that fit best to each or these categories. We then explore representative models in further detail, from traditional statistical models to recent kernel models, presenting several up-to-date research projects in bioinfomatics to exemplify how biological questions can benefit from a machine learning approach. Finally, we discuss how cooperation between biologis ts and machine learners might be made smoother.

[BibTex]

Shin, H. Machine Learning and Applications in Biology In BioKorea 2007, pages: 337-366, BioKorea, September 2007 (inproceedings)

[BibTex]

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A Nonparametric Approach to Bottom-Up Visual Saliency

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M.

In Advances in Neural Information Processing Systems 19, pages: 689-696, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
This paper addresses the bottom-up influence of local image information on human eye movements. Most existing computational models use a set of biologically plausible linear filters, e.g., Gabor or Difference-of-Gaussians filters as a front-end, the outputs of which are nonlinearly combined into a real number that indicates visual saliency. Unfortunately, this requires many design parameters such as the number, type, and size of the front-end filters, as well as the choice of nonlinearities, weighting and normalization schemes etc., for which biological plausibility cannot always be justified. As a result, these parameters have to be chosen in a more or less ad hoc way. Here, we propose to emph{learn} a visual saliency model directly from human eye movement data. The model is rather simplistic and essentially parameter-free, and therefore contrasts recent developments in the field that usually aim at higher prediction rates at the cost of additional parameters and increasing model complexity. Experimental results show that - despite the lack of any biological prior knowledge - our model performs comparably to existing approaches, and in fact learns image features that resemble findings from several previous studies. In particular, its maximally excitatory stimuli have center-surround structure, similar to receptive fields in the early human visual system.

PDF Web [BibTex]

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M. A Nonparametric Approach to Bottom-Up Visual Saliency In Advances in Neural Information Processing Systems 19, pages: 689-696, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Information Bottleneck for Non Co-Occurrence Data

Seldin, Y., Slonim, N., Tishby, N.

In Advances in Neural Information Processing Systems 19, pages: 1241-1248, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z(X,Y). For example, in gene expression data, the expression level Z is a function of gene X and condition Y; or in movie ratings data the rating Z is a function of viewer X and movie Y . The approach represents a consistent extension of the Information Bottleneck method that has previously relied on the availability of co-occurrence statistics. By altering the relevance variable we eliminate the need in the sample of joint distribution of all input variables. This new formulation also enables simple MDL-like model complexity control and prediction of missing values of Z. The approach is analyzed and shown to be on a par with the best known clustering algorithms for a wide range of domains. For the prediction of missing values (collaborative filtering) it improves the currently best known results.

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Seldin, Y., Slonim, N., Tishby, N. Information Bottleneck for Non Co-Occurrence Data In Advances in Neural Information Processing Systems 19, pages: 1241-1248, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Learning with Hypergraphs: Clustering, Classification, and Embedding

Zhou, D., Huang, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 19, pages: 1601-1608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are more complex than pairwise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for our learning tasks however. Therefore we consider using hypergraphs instead to completely represent complex relationships among the objects of our interest, and thus the problem of learning with hypergraphs arises. Our main contribution in this paper is to generalize the powerful methodology of spectral clustering which originally operates on undirected graphs to hypergraphs, and further develop algorithms for hypergraph embedding and transductive classi¯cation on the basis of the spectral hypergraph clustering approach. Our experiments on a number of benchmarks showed the advantages of hypergraphs over usual graphs.

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Zhou, D., Huang, J., Schölkopf, B. Learning with Hypergraphs: Clustering, Classification, and Embedding In Advances in Neural Information Processing Systems 19, pages: 1601-1608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals

Hill, N., Lal, T., Tangermann, M., Hinterberger, T., Widman, G., Elger, C., Schölkopf, B., Birbaumer, N.

In Toward Brain-Computer Interfacing, pages: 235-260, Neural Information Processing, (Editors: G Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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Hill, N., Lal, T., Tangermann, M., Hinterberger, T., Widman, G., Elger, C., Schölkopf, B., Birbaumer, N. Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals In Toward Brain-Computer Interfacing, pages: 235-260, Neural Information Processing, (Editors: G Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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Fast Computation of Graph Kernels

Vishwanathan, SVN., Borgwardt, KM., Schraudolph, N.

In Advances in Neural Information Processing Systems 19, pages: 1449-1456, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us to compute many of these kernels in O(n3) worst-case time. This includes kernels whose previous worst-case time complexity was O(n6), such as the geometric kernels of G¨artner et al. [1] and the marginal graph kernels of Kashima et al. [2]. Our algebra in RKHS allow us to exploit sparsity in directed and undirected graphs more effectively than previous methods, yielding sub-cubic computational complexity when combined with conjugate gradient solvers or fixed-point iterations. Experiments on graphs from bioinformatics and other application domains show that our algorithms are often more than 1000 times faster than existing approaches.

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Vishwanathan, SVN., Borgwardt, KM., Schraudolph, N. Fast Computation of Graph Kernels In Advances in Neural Information Processing Systems 19, pages: 1449-1456, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Joint Kernel Maps

Weston, J., Bakir, G., Bousquet, O., Mann, T., Noble, W., Schölkopf, B.

In Predicting Structured Data, pages: 67-84, Advances in neural information processing systems, (Editors: GH Bakir and T Hofmann and B Schölkopf and AJ Smola and B Taskar and SVN Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Web [BibTex]

Weston, J., Bakir, G., Bousquet, O., Mann, T., Noble, W., Schölkopf, B. Joint Kernel Maps In Predicting Structured Data, pages: 67-84, Advances in neural information processing systems, (Editors: GH Bakir and T Hofmann and B Schölkopf and AJ Smola and B Taskar and SVN Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Web [BibTex]

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Correcting Sample Selection Bias by Unlabeled Data

Huang, J., Smola, A., Gretton, A., Borgwardt, K., Schölkopf, B.

In Advances in Neural Information Processing Systems 19, pages: 601-608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract
We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.

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Huang, J., Smola, A., Gretton, A., Borgwardt, K., Schölkopf, B. Correcting Sample Selection Bias by Unlabeled Data In Advances in Neural Information Processing Systems 19, pages: 601-608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

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Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach

Hinterberger, T., Nijboer, F., Kübler, A., Matuz, T., Furdea, A., Mochty, U., Jordan, M., Lal, T., Hill, J., Mellinger, J., Bensch, M., Tangermann, M., Widman, G., Elger, C., Rosenstiel, W., Schölkopf, B., Birbaumer, N.

In Toward Brain-Computer Interfacing, pages: 43-64, Neural Information Processing, (Editors: G. Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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Hinterberger, T., Nijboer, F., Kübler, A., Matuz, T., Furdea, A., Mochty, U., Jordan, M., Lal, T., Hill, J., Mellinger, J., Bensch, M., Tangermann, M., Widman, G., Elger, C., Rosenstiel, W., Schölkopf, B., Birbaumer, N. Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach In Toward Brain-Computer Interfacing, pages: 43-64, Neural Information Processing, (Editors: G. Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

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Collaborative Filtering via Ensembles of Matrix Factorizations

Wu, M.

In KDD Cup and Workshop 2007, pages: 43-47, KDD Cup and Workshop, August 2007 (inproceedings)

Abstract
We present a Matrix Factorization(MF) based approach for the Netflix Prize competition. Currently MF based algorithms are popular and have proved successful for collaborative filtering tasks. For the Netflix Prize competition, we adopt three different types of MF algorithms: regularized MF, maximum margin MF and non-negative MF. Furthermore, for each MF algorithm, instead of selecting the optimal parameters, we combine the results obtained with several parameters. With this method, we achieve a performance that is more than 6% better than the Netflix‘s own system.

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Wu, M. Collaborative Filtering via Ensembles of Matrix Factorizations In KDD Cup and Workshop 2007, pages: 43-47, KDD Cup and Workshop, August 2007 (inproceedings)

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