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2007


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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]

2007

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]

PDF Web DOI [BibTex]


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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]

[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]

PDF Web [BibTex]


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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.

PDF Web [BibTex]

PDF Web [BibTex]


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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.

PDF Web [BibTex]

PDF Web [BibTex]


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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.

PDF Web [BibTex]

PDF 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.

PDF Web [BibTex]

PDF Web [BibTex]


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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.

PDF Web [BibTex]

PDF Web [BibTex]


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Thinking Out Loud: Research and Development of Brain Computer Interfaces

Hill, NJ.

Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

Abstract
My principal interest is in applying machine-learning methods to the development of Brain-Computer Interfaces (BCI). This involves the classification of a user‘s intentions or mental states, or regression against some continuous intentional control signal, using brain signals obtained for example by EEG, ECoG or MEG. The long-term aim is to develop systems that a completely paralysed person (such as someone suffering from advanced Amyotrophic Lateral Sclerosis) could use to communicate. Such systems have the potential to improve the lives of many people who would be otherwise completely unable to communicate, but they are still very much in the research and development stages.

PDF [BibTex]

PDF [BibTex]


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Fusion of spectral and spatial information by a novel SVM classification technique

Bruzzone, L., Marconcini, M., Persello, C.

In pages: 4838-4841 , IEEE, Piscataway, NJ, USA, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2007 (inproceedings)

Abstract
A novel context-sensitive semisupervised classification technique based on support vector machines is proposed. This technique aims at exploiting the SVM method for image classification by properly fusing spectral information with spatial- context information. This results in: i) an increased robustness to noisy training sets in the learning phase of the classifier; ii) a higher and more stable classification accuracy with respect to the specific patterns included in the training set; and iii) a regularized classification map. The main property of the proposed context sensitive semisupervised SVM (CS4VM) is to adaptively exploit the contextual information in the training phase of the classifier, without any critical assumption on the expected labels of the pixels included in the same neighborhood system. This is done by defining a novel context-sensitive term in the objective function used in the learning of the classifier. In addition, the proposed CS4VM can be integrated with a Markov random field (MRF) approach for exploiting the contextual information also to regularize the classification map. Experiments carried out on very high geometrical resolution images confirmed the effectiveness of the proposed technique.

Web DOI [BibTex]

Web DOI [BibTex]


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Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces

Deisenroth, MP., Weissel, F., Ohtsuka, T., Hanebeck, UD.

In ECC‘07, pages: 3664-3671, 9th European Control Conference, July 2007 (inproceedings)

Abstract
A novel online-computation approach to optimal control of nonlinear, noise-affected systems with continuous state and control spaces is presented. In the proposed algorithm, system noise is explicitly incorporated into the control decision. This leads to superior results compared to state-of-the-art nonlinear controllers that neglect this influence. The solution of an optimal nonlinear controller for a corresponding deterministic system is employed to find a meaningful state space restriction. This restriction is obtained by means of approximate state prediction using the noisy system equation. Within this constrained state space, an optimal closed-loop solution for a finite decision-making horizon (prediction horizon) is determined within an adaptively restricted optimization space. Interleaving stochastic dynamic programming and value function approximation yields a solution to the considered optimal control problem. The enhanced performance of the proposed discrete-time controller is illustrated by means o f a scalar example system. Nonlinear model predictive control is applied to address approximate treatment of infinite-horizon problems by the finite-horizon controller.

PDF Web [BibTex]

PDF Web [BibTex]


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Error Correcting Codes for the P300 Visual Speller

Biessmann, F.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2007 (diplomathesis)

Abstract
The aim of brain-computer interface (BCI) research is to establish a communication system based on intentional modulation of brain activity. This is accomplished by classifying patterns of brain ac- tivity, volitionally induced by the user. The BCI presented in this study is based on a classical paradigm as proposed by (Farwell and Donchin, 1988), the P300 visual speller. Recording electroencephalo- grams (EEG) from the scalp while presenting letters successively to the user, the speller can infer from the brain signal which letter the user was focussing on. Since EEG recordings are noisy, usually many repetitions are needed to detect the correct letter. The focus of this study was to improve the accuracy of the visual speller applying some basic principles from information theory: Stimulus sequences of the speller have been modified into error-correcting codes. Additionally a language model was incorporated into the probabilistic letter de- coder. Classification of single EEG epochs was less accurate using error correcting codes. However, the novel code could compensate for that such that overall, letter accuracies were as high as or even higher than for classical stimulus codes. In particular at high noise levels, error-correcting decoding achieved higher letter accuracies.

PDF [BibTex]

PDF [BibTex]


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A Kernel Approach to Comparing Distributions

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

In Proceedings of the 22. AAAI Conference on Artificial Intelligence, pages: 1637-1641, AAAI Press, Menlo Park, CA, USA, Twenty-Second AAAI Conference on Artificial Intelligence (AAAI), July 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. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

PDF Web [BibTex]

PDF Web [BibTex]


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Manifold Denoising as Preprocessing for Finding Natural Representations of Data

Hein, M., Maier, M.

In AAAI-07, pages: 1646-1649, AAAI Press, Menlo Park, CA, USA, Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07), July 2007 (inproceedings)

Abstract
A natural representation of data are the parameters which generated the data. If the parameter space is continuous we can regard it as a manifold. In practice we usually do not know this manifold but we just have some representation of the data, often in a very high-dimensional feature space. Since the number of internal parameters does not change with the representation, the data will effectively lie on a low-dimensional submanifold in feature space. Due to measurement errors this data is usually corrupted by noise which particularly in high-dimensional feature spaces makes it almost impossible to find the manifold structure. This paper reviews a method called Manifold Denoising which projects the data onto the submanifold using a diffusion process on a graph generated by the data. We will demonstrate that the method is capable of dealing with non-trival high-dimensional noise. Moreover we will show that using the method as a preprocessing step one can significantly improve the results of a semi-supervised learning algorithm.

PDF Web [BibTex]

PDF Web [BibTex]


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Graph-based Protein Functional Classification

Shin, H., Lisewski, A., Lichtarge, O.

In BIOCOMP‘07, pages: 738-744, (Editors: Arabnia, H. R., M. Q. Yang, J. Y. Yang), CSREA Press, Las Vegas, NV, USA, 2007 International Conference on Bioinformatics and Computational Biology, July 2007 (inproceedings)

Web [BibTex]

Web [BibTex]


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Data-driven goodness-of-fit tests

Langovoy, MA.

Biologische Kybernetik, Georg-August-Universität Göttingen, Göttingen, Germany, July 2007 (phdthesis)

Web [BibTex]

Web [BibTex]


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Supervised Feature Selection via Dependence Estimation

Song, L., Smola, A., Gretton, A., Borgwardt, K., Bedo, J.

In Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), pages: 823-830, (Editors: Ghahramani, Z. ), ACM Press, New York, NY, USA, Twenty-Fourth Annual International Conference on Machine Learning (ICML), June 2007 (inproceedings)

Abstract
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A Kernel-Based Causal Learning Algorithm

Sun, X., Janzing, D., Schölkopf, B., Fukumizu, K.

In Proceedings of the 24th International Conference on Machine Learning, pages: 855-862, (Editors: Z Ghahramani), ACM Press, New York, NY, USA, ICML, June 2007 (inproceedings)

Abstract
We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraint-based PC algorithm.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Entire Regularization Paths for Graph Data

Tsuda, K.

In ICML 2007, pages: 919-926, (Editors: Ghahramani, Z. ), ACM Press, New York, NY, USA, 24th Annual International Conference on Machine Learning, June 2007 (inproceedings)

Abstract
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible subgraph patterns, the dimensionality gets too large for usual statistical methods. We propose an efficient method to select a small number of salient patterns by regularization path tracking. The generation of useless patterns is minimized by progressive extension of the search space. In experiments, it is shown that our technique is considerably more efficient than a simpler approach based on frequent substructure mining.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Les Représentations Prédictives des États et des Politiques

Boularias, A., Chaib-Draa, B.

In MFI 2007, pages: 37-48, Quatrièmes Journées Francophones Modèles Formels de l‘Interaction, June 2007 (inproceedings)

Abstract
Nous proposons dans cet article une nouvelle approche pour représenter les politiques (stratégies) dans les environnements stochastiques et partiellement observables. Nous nous intéressons plus particulièrement aux systèmes multi-agents, où chaque agent connaît uniquement ses propres politiques, et doit choisir la meilleure parmi elles selon son état de croyance sur les politiques du reste des agents. Notre modèle utilise moins de paramètres que les méthodes de représentation usuelles, telles que les arbres de décision ou les contrôleurs d’états finis stochastiques, permettant ainsi une accélération des algorithmes de planification. Nous montrons aussi comment ce modèle peut être utilisé efficacement dans le cas de la planification multiagents coopérative et sans communication, les résultats empiriques sont comparés avec le modèle DEC-POMDP (Decentralized Partially Observable Markov Decision Process).

PDF Web [BibTex]

PDF Web [BibTex]


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Dirichlet Process Mixtures of Factor Analysers

Görür, D., Rasmussen, C.

Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

Abstract
Mixture of factor analysers (MFA) is a well-known model that combines the dimensionality reduction technique of Factor Analysis (FA) with mixture modeling. The key issue in MFA is deciding on the latent dimension and the number of mixture components to be used. The Bayesian treatment of MFA has been considered by Beal and Ghahramani (2000) using variational approximation and by Fokoué and Titterington (2003) using birth-and –death Markov chain Monte Carlo (MCMC). Here, we present the nonparametric MFA model utilizing a Dirichlet process (DP) prior on the component parameters (that is, the factor loading matrix and the mean vector of each component) and describe an MCMC scheme for inference. The clustering property of the DP provides automatic selection of the number of mixture components. The latent dimensionality of each component is inferred by automatic relevance determination (ARD). Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging clustering problem. We apply our model for clustering the waveforms recorded from the cortex of a macaque monkey.

Web [BibTex]

Web [BibTex]


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An Extensible Probabilistic Transformation-based Approach to the Third Recognizing Textual Entailment Challenge

Harmeling, S.

In TextEntail 2007, pages: 137-142, ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, June 2007 (inproceedings)

Abstract
We introduce a system for textual entailment that is based on a probabilistic model of entailment. The model is defined using some calculus of transformations on dependency trees, which is characterized by the fact that derivations in that calculus preserve the truth only with a certain probability. We also describe a possible set of transformations (and with it implicitly a calculus) that was successfully applied to the RTE3 challenge data. However, our system can be improved in many ways and we see it as the starting point for a promising new approach to textual entailment.

Web [BibTex]

Web [BibTex]


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Weighted Substructure Mining for Image Analysis

Nowozin, S., Tsuda, K., Uno, T., Kudo, T., BakIr, G.

In CVPR 2007, pages: 1-8, IEEE Computer Society, Los Alamitos, CA, USA, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2007 (inproceedings)

Abstract
In web-related applications of image categorization, it is desirable to derive an interpretable classification rule with high accuracy. Using the bag-of-words representation and the linear support vector machine, one can partly fulfill the goal, but the accuracy of linear classifiers is not high and the obtained features are not informative for users. We propose to combine item set mining and large margin classifiers to select features from the power set of all visual words. Our resulting classification rule is easier to browse and simpler to understand, because each feature has richer information. As a next step, each image is represented as a graph where nodes correspond to local image features and edges encode geometric relations between features. Combining graph mining and boosting, we can obtain a classification rule based on subgraph features that contain more information than the set features. We evaluate our algorithm in a web-retrieval ranking task where the goal is to reject outliers from a set of images returned for a keyword query. Furthermore, it is evaluated on the supervised classification tasks with the challenging VOC2005 data set. Our approach yields excellent accuracy in the unsupervised ranking task compared to a recently proposed probabilistic model and competitive results in the supervised classification task.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Local Learning Projections

Wu, M., Yu, K., Yu, S., Schölkopf, B.

In Proceedings of the 24th International Conference on Machine Learning, pages: 1039-1046, (Editors: Z Ghahramani), ACM Press, New York, NY, USA, ICML, June 2007 (inproceedings)

Abstract
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the minimal local estimation error, and elucidate its advantages for classification tasks. We also indicate that LLP keeps the local information in the sense that the projection value of each point can be well estimated based on its neighbors and their projection values. Experimental results are provided to validate the effectiveness of the proposed algorithm.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Training and Approximation of a Primal Multiclass Support Vector Machine

Zien, A., Bona, F., Ong, C.

In ASMDA 2007, pages: 1-8, (Editors: Skiadas, C. H.), 12th International Conference on Applied Stochastic Models and Data Analysis, June 2007 (inproceedings)

Abstract
We revisit the multiclass support vector machine (SVM) and generalize the formulation to convex loss functions and joint feature maps. Motivated by recent work [Chapelle, 2006] we use logistic loss and softmax to enable gradient based primal optimization. Kernels are incorporated via kernel principal component analysis (KPCA), which naturally leads to approximation methods for large scale problems. We investigate similarities and differences to previous multiclass SVM approaches. Experimental comparisons to previous approaches and to the popular one-vs-rest SVM are presented on several different datasets.

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams

Hill, N., Raths, C.

Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

Abstract
When considering Brain-Computer Interface (BCI) development for patients in the most severely paralysed states, there is considerable motivation to move away from BCI systems based on either motor cortex activity, or on visual stimuli. Together these account for most of current BCI research. I present the results of our recent exploration of new auditory- and tactile-stimulus-driven BCIs. The talk includes a tutorial on the construction and interpretation of classifiers which extract spatio-temporal features from event-related potential data. The effects and implications of whitening are discussed, and preliminary results on the effectiveness of a low-rank constraint (Tomioka and Aihara 2007) are shown.

PDF Web [BibTex]

PDF Web [BibTex]


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Towards Motor Skill Learning in Robotics

Peters, J.

Interactive Robot Learning - RSS workshop, June 2007 (talk)

Web [BibTex]

Web [BibTex]


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Nonlinear independent component analysis with minimum nonlinear distortion

Zhang, K., Chan, L.

In ICML ’07: Proceedings of the 24th international conference on Machine learning, pages: 1127-1134, (Editors: Z Ghahramani), ACM, New York, NY, USA, 24th International Conference on Machine Learning (ICML), June 2007 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Information-theoretic Metric Learning

Davis, J., Kulis, B., Jain, P., Sra, S., Dhillon, I.

In ICML 2007, pages: 209-216, (Editors: Ghahramani, Z. ), ACM Press, New York, NY, USA, 24th Annual International Conference on Machine Learning, June 2007 (inproceedings)

Abstract
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem---that of minimizing the LogDet divergence subject to linear constraints. Our resulting algorithm has several advantages over existing methods. First, our method can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Second, it is fast and scalable. Unlike most existing methods, no eigenvalue computations or semi-definite programming are required. We also present an online version and derive regret bounds for the resulting algorithm. Finally, we evaluate our method on a recent error reporting system for software called Clarify, in the context of metric learning for nearest neighbor classification, as well as on standard data sets.

Web DOI [BibTex]

Web DOI [BibTex]


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A Dependence Maximization View of Clustering

Song, L., Smola, A., Gretton, A., Borgwardt, K.

In Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), pages: 815-822, (Editors: Ghahramani, Z. ), ACM Press, New York, NY, USA, Twenty-Fourth Annual International Conference on Machine Learning (ICML), June 2007 (inproceedings)

Abstract
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Independence Criterion (HSIC). Under this framework, we unify the geometric, spectral, and statistical dependence views of clustering, and subsume many existing algorithms as special cases (e.g. k-means and spectral clustering). Distinctive to our framework is that kernels can also be applied on the labels, which can endow them with particular structures. We also obtain a perturbation bound on the change in k-means clustering.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Multiclass Multiple Kernel Learning

Zien, A., Ong, C.

In ICML 2007, pages: 1191-1198, (Editors: Ghahramani, Z. ), ACM Press, New York, NY, USA, 24th International Conference on Machine Learning, June 2007 (inproceedings)

Abstract
In many applications it is desirable to learn from several kernels. “Multiple kernel learning” (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature maps. This provides a convenient and principled way for MKL with multiclass problems. In addition, we can exploit the joint feature map to learn kernels on output spaces. We show the equivalence of several different primal formulations including different regularizers. We present several optimization methods, and compare a convex quadratically constrained quadratic program (QCQP) and two semi-infinite linear programs (SILPs) toy data, showing that the SILPs are faster than the QCQP. We then demonstrate the utility of our method by applying the SILP to three real world datasets.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Transductive Support Vector Machines for Structured Variables

Zien, A., Brefeld, U., Scheffer, T.

In ICML 2007, pages: 1183-1190, (Editors: Ghahramani, Z. ), ACM Press, New York, NY, USA, 24th International Conference on Machine Learning, June 2007 (inproceedings)

Abstract
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Transductive Support Vector Machines for Structured Variables

Zien, A., Brefeld, U., Scheffer, T.

International Conference on Machine Learning (ICML), June 2007 (talk)

Abstract
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.

PDF PDF Web [BibTex]

PDF PDF Web [BibTex]


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Competition and Coordination in Stochastic Games

Burkov, A., Boularias, A., Chaib-Draa, B.

In Canadian AI 2007, pages: 26-37, (Editors: Kobti, Z. , D. Wu), Springer, Berlin, Germany, 20th Conference of the Canadian Society for Computational Studies of Intelligence, May 2007 (inproceedings)

Abstract
Agent competition and coordination are two classical and most important tasks in multiagent systems. In recent years, there was a number of learning algorithms proposed to resolve such type of problems. Among them, there is an important class of algorithms, called adaptive learning algorithms, that were shown to be able to converge in self-play to a solution in a wide variety of the repeated matrix games. Although certain algorithms of this class, such as Infinitesimal Gradient Ascent (IGA), Policy Hill-Climbing (PHC) and Adaptive Play Q-learning (APQ), have been catholically studied in the recent literature, a question of how these algorithms perform versus each other in general form stochastic games is remaining little-studied. In this work we are trying to answer this question. To do that, we analyse these algorithms in detail and give a comparative analysis of their behavior on a set of competition and coordination stochastic games. Also, we introduce a new multiagent learning algorithm, called ModIGA. This is an extension of the IGA algorithm, which is able to estimate the strategy of its opponents in the cases when they do not explicitly play mixed strategies (e.g., APQ) and which can be applied to the games with more than two actions.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Impact of target-to-target interval on classification performance in the P300 speller

Martens, S., Hill, J., Farquhar, J., Schölkopf, B.

Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

PDF Web [BibTex]

PDF Web [BibTex]


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Benchmarking of Policy Gradient Methods

Peters, J.

ADPRL Workshop, April 2007 (talk)

[BibTex]

[BibTex]


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Change-Point Detection using Krylov Subspace Learning

Ide, T., Tsuda, K.

In SDM 2007, pages: 515-520, (Editors: Apte, C. ), Society for Industrial and Applied Mathematics, Pittsburgh, PA, USA, SIAM International Conference on Data Mining, April 2007 (inproceedings)

Abstract
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace learning works well both in matrix compression and implicit calculation of the inner product by taking full advantage of the arbitrariness of the seed vector. We apply our algorithm to a PCA-based change-point detection algorithm, and show that it results in about 50 times improvement in computational time.

PDF Web [BibTex]

PDF Web [BibTex]


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Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning

Görür, D.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, April 2007, published online (phdthesis)

PDF PDF [BibTex]

PDF PDF [BibTex]


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A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics

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

In RSS 2006, pages: 247-254, (Editors: Sukhatme, G. S., S. Schaal, W. Burgard, D. Fox), MIT Press, Cambridge, MA, USA, Robotics: Science and Systems II (RSS ), April 2007 (inproceedings)

Abstract
For robots of increasing complexity such as humanoid robots, conventional identification of rigid body dynamics models based on CAD data and actuator models becomes difficult and inaccurate due to the large number of additional nonlinear effects in these systems, e.g., stemming from stiff wires, hydraulic hoses, protective shells, skin, etc. Data driven parameter estimation offers an alternative model identification method, but it is often burdened by various other problems, such as significant noise in all measured or inferred variables of the robot. The danger of physically inconsistent results also exists due to unmodeled nonlinearities or insufficiently rich data. In this paper, we address all these problems by developing a Bayesian parameter identification method that can automatically detect noise in both input and output data for the regression algorithm that performs system identification. A post-processing step ensures physically consistent rigid body parameters by nonlinearly projecting the result of the Bayesian estimation onto constraints given by positive definite inertia matrices and the parallel axis theorem. We demonstrate on synthetic and actual robot data that our technique performs parameter identification with 5 to 20% higher accuracy than traditional methods. Due to the resulting physically consistent parameters, our algorithm enables us to apply advanced control methods that algebraically require physical consistency on robotic platforms.

PDF Web [BibTex]

PDF Web [BibTex]


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Learning causality by identifying common effects with kernel-based dependence measures

Sun, X., Janzing, D.

In ESANN 2007, pages: 453-458, D-Side, Evere, Belgium, 15th European Symposium on Artificial Neural Networks, April 2007 (inproceedings)

Abstract
We describe a method for causal inference that measures the strength of statistical dependence by the Hilbert-Schmidt norm of kernel-based conditional cross-covariance operators. We consider the increase of the dependence of two variables X and Y by conditioning on a third variable Z as a hint for Z being a common effect of X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges according to the majority vote. For most of our experiments with artificial and real-world data our method has outperformed the conventional constraint-based inductive causation (IC) algorithm.

PDF Web [BibTex]

PDF Web [BibTex]


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Exploring the causal order of binary variables via exponential hierarchies of Markov kernels

Sun, X., Janzing, D.

In ESANN 2007, pages: 465-470, D-Side, Evere, Belgium, 15th European Symposium on Artificial Neural Networks, April 2007 (inproceedings)

Abstract
We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n>=4) binary variables X_1,...,X_n. Our inference principle states that the factorization of the joint probability into conditional probabilities for X_j given X_1,...,X_{j-1} often leads to simpler terms if the order of variables is compatible with the directed acyclic graph representing the causal structure. We study joint measures of OR/AND gates and show that the complexity of the conditional probabilities (the so-called Markov kernels), defined by a hierarchy of exponential models, depends on the order of the variables. Some toy and real-data experiments support our inference rule.

PDF Web [BibTex]

PDF Web [BibTex]


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Applying the Episodic Natural Actor-Critic Architecture to Motor Primitive Learning

Peters, J., Schaal, S.

In Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), pages: 295-300, D-Side, Evere, Belgium, 15th European Symposium on Artificial Neural Networks (ESANN), April 2007 (inproceedings)

Abstract
In this paper, we investigate motor primitive learning with the Natural Actor-Critic approach. The Natural Actor-Critic consists out of actor updates which are achieved using natural stochastic policy gradients while the critic obtains the natural policy gradient by linear regression. We show that this architecture can be used to learn the “building blocks of movement generation”, called motor primitives. Motor primitives are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties. We show that our most modern algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.

PDF Web [BibTex]

PDF Web [BibTex]


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Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem

Kim, D., Sra, S., Dhillon, I.

In SDM 2007, pages: 343-354, (Editors: Apte, C. ), Society for Industrial and Applied Mathematics, Pittsburgh, PA, USA, SIAM International Conference on Data Mining, April 2007 (inproceedings)

Abstract
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to be useful for a wide variety of applications ranging from document analysis and image processing to bioinformatics. There exist a few algorithms for nonnegative matrix approximation (NNMA), for example, Lee & Seung’s multiplicative updates, alternating least squares, and certain gradient descent based procedures. All of these procedures suffer from either slow convergence, numerical instabilities, or at worst, theoretical unsoundness. In this paper we present new and improved algorithms for the least-squares NNMA problem, which are not only theoretically well-founded, but also overcome many of the deficiencies of other methods. In particular, we use non-diagonal gradient scaling to obtain rapid convergence. Our methods provide numerical results superior to both Lee & Seung’s method as well to the alternating least squares (ALS) heuristic, which is known to work well in some situations but has no theoretical guarantees (Berry et al. 2006). Our approach extends naturally to include regularization and box-constraints, without sacrificing convergence guarantees. We present experimental results on both synthetic and realworld datasets to demonstrate the superiority of our methods, in terms of better approximations as well as efficiency.

PDF Web [BibTex]

PDF Web [BibTex]


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Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions

Sun, X., Janzing, D., Schölkopf, B.

In Proceedings of the 15th European Symposium on Artificial Neural Networks , pages: 441-446, (Editors: M Verleysen), D-Side Publications, Evere, Belgium, ESANN, April 2007 (inproceedings)

Abstract
We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities. The motivation is to provide a tool for a causal inference method which assumes that conditional probabilities for effects given their causes are typically simpler and smoother than vice-versa. We present experiments with toy data where the quantitative results are consistent with our intuitive understanding of complexity and smoothness. Also in some examples with real-world data the probability measure corresponding to the true causal direction turned out to be less complex than those of the reversed order.

PDF Web [BibTex]

PDF Web [BibTex]


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Deterministic Annealing for Multiple-Instance Learning

Gehler, P., Chapelle, O.

In JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007, pages: 123-130, (Editors: Meila, M. , X. Shen), MIT Press, Cambridge, MA, USA, 11th International Conference on Artificial Intelligence and Statistics, March 2007 (inproceedings)

Abstract
In this paper we demonstrate how deterministic annealing can be applied to different SVM formulations of the multiple-instance learning (MIL) problem. Our results show that we find better local minima compared to the heuristic methods those problems are usually solved with. However this does not always translate into a better test error suggesting an inadequacy of the objective function. Based on this finding we propose a new objective function which together with the deterministic annealing algorithm finds better local minima and achieves better performance on a set of benchmark datasets. Furthermore the results also show how the structure of MIL datasets influence the performance of MIL algorithms and we discuss how future benchmark datasets for the MIL problem should be designed.

PDF Web [BibTex]

PDF Web [BibTex]


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Bayesian Inference and Optimal Design in the Sparse Linear Model

Seeger, M., Steinke, F., Tsuda, K.

In JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007, pages: 444-451, (Editors: Meila, M. , X. Shen), JMLR, Cambridge, MA, USA, 11th International Conference on Artificial Intelligence and Statistics, March 2007 (inproceedings)

Abstract
The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian inference by expensive Markov chain Monte Carlo, or replaced it by point estimation. We show how to obtain a good approximation to Bayesian analysis efficiently, using the Expectation Propagation method. We also address the problems of optimal design and hyperparameter estimation. We demonstrate our framework on a gene network identification task.

PDF Web [BibTex]

PDF Web [BibTex]


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Stick-breaking Construction for the Indian Buffet Process

Teh, Y., Görür, D., Ghahramani, Z.

In JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007, pages: 556-563, (Editors: Meila, M. , X. Shen), MIT Press, Cambridge, MA, USA, 11th International Conference on Artificial Intelligence and Statistics, March 2007 (inproceedings)

Abstract
The Indian buffet process (IBP) is a Bayesian nonparametric distribution whereby objects are modelled using an unbounded number of latent features. In this paper we derive a stick-breaking representation for the IBP. Based on this new representation, we develop slice samplers for the IBP that are efficient, easy to implement and are more generally applicable than the currently available Gibbs sampler. This representation, along with the work of Thibaux and Jordan [17], also illuminates interesting theoretical connections between the IBP, Chinese restaurant processes, Beta processes and Dirichlet processes.

PDF Web [BibTex]

PDF Web [BibTex]