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2011


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Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation

Görnitz, N., Widmer, C., Zeller, G., Kahles, A., Sonnenburg, S., Rätsch, G.

In Advances in Neural Information Processing Systems 24, pages: 2690-2698, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and FCN Pereira and KQ Weinberger), Curran Associates, Inc., Red Hook, NY, USA, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

PDF [BibTex]

2011

PDF [BibTex]


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Phase transition in the family of p-resistances

Alamgir, M., von Luxburg, U.

In Advances in Neural Information Processing Systems 24, pages: 379-387, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract
We study the family of p-resistances on graphs for p ≥ 1. This family generalizes the standard resistance distance. We prove that for any fixed graph, for p=1, the p-resistance coincides with the shortest path distance, for p=2 it coincides with the standard resistance distance, and for p → ∞ it converges to the inverse of the minimal s-t-cut in the graph. Secondly, we consider the special case of random geometric graphs (such as k-nearest neighbor graphs) when the number n of vertices in the graph tends to infinity. We prove that an interesting phase-transition takes place. There exist two critical thresholds p^* and p^** such that if p < p^*, then the p-resistance depends on meaningful global properties of the graph, whereas if p > p^**, it only depends on trivial local quantities and does not convey any useful information. We can explicitly compute the critical values: p^* = 1 + 1/(d-1) and p^** = 1 + 1/(d-2) where d is the dimension of the underlying space (we believe that the fact that there is a small gap between p^* and p^** is an artifact of our proofs. We also relate our findings to Laplacian regularization and suggest to use q-Laplacians as regularizers, where q satisfies 1/p^* + 1/q = 1.

PDF Web [BibTex]

PDF Web [BibTex]


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On Fast Approximate Submodular Minimization

Jegelka, S., Lin, H., Bilmes, J.

In Advances in Neural Information Processing Systems 24, pages: 460-468, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract
We are motivated by an application to extract a representative subset of machine learning training data and by the poor empirical performance we observe of the popular minimum norm algorithm. In fact, for our application, minimum norm can have a running time of about O(n7) (O(n5) oracle calls). We therefore propose a fast approximate method to minimize arbitrary submodular functions. For a large sub-class of submodular functions, the algorithm is exact. Other submodular functions are iteratively approximated by tight submodular upper bounds, and then repeatedly optimized. We show theoretical properties, and empirical results suggest significant speedups over minimum norm while retaining higher accuracies.

PDF Web [BibTex]

PDF Web [BibTex]


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PAC-Bayesian Analysis of Contextual Bandits

Seldin, Y., Auer, P., Laviolette, F., Shawe-Taylor, J., Ortner, R.

In Advances in Neural Information Processing Systems 24, pages: 1683-1691, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract
We derive an instantaneous (per-round) data-dependent regret bound for stochastic multiarmed bandits with side information (also known as contextual bandits). The scaling of our regret bound with the number of states (contexts) $N$ goes as $\sqrt{N I_{\rho_t}(S;A)}$, where $I_{\rho_t}(S;A)$ is the mutual information between states and actions (the side information) used by the algorithm at round $t$. If the algorithm uses all the side information, the regret bound scales as $\sqrt{N \ln K}$, where $K$ is the number of actions (arms). However, if the side information $I_{\rho_t}(S;A)$ is not fully used, the regret bound is significantly tighter. In the extreme case, when $I_{\rho_t}(S;A) = 0$, the dependence on the number of states reduces from linear to logarithmic. Our analysis allows to provide the algorithm large amount of side information, let the algorithm to decide which side information is relevant for the task, and penalize the algorithm only for the side information that it is using de facto. We also present an algorithm for multiarmed bandits with side information with computational complexity that is a linear in the number of actions.

PDF PDF Web [BibTex]

PDF PDF Web [BibTex]


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Fast projections onto L1,q-norm balls for grouped feature selection

Sra, S.

In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, LNCS vol 6913, Part III, pages: 305-317, (Editors: D Gunopulos and T Hofmann and D Malerba and M Vazirgiannis), Springer, 22th European Conference on Machine Learning (ECML), 2011 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Model Learning in Robot Control

Nguyen-Tuong, D.

Albert-Ludwigs-Universität Freiburg, Germany, 2011 (phdthesis)

[BibTex]

[BibTex]


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Kernel Belief Propagation

Song, L., Gretton, A., Bickson, D., Low, Y., Guestrin, C.

In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Vol. 15, pages: 707-715, (Editors: G Gordon and D Dunson and M Dudík), JMLR, AISTATS, 2011 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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On Causal Discovery with Cyclic Additive Noise Models

Mooij, J., Janzing, D., Schölkopf, B., Heskes, T.

In Advances in Neural Information Processing Systems 24, pages: 639-647, (Editors: J Shawe-Taylor and RS Zemel and PL Bartlett and FCN Pereira and KQ Weinberger), Curran Associates, Inc., Red Hook, NY, USA, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract
We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise. We prove that the causal graph of such models is generically identifiable in the bivariate, Gaussian-noise case. We also propose a method to learn such models from observational data. In the acyclic case, the method reduces to ordinary regression, but in the more challenging cyclic case, an additional term arises in the loss function, which makes it a special case of nonlinear independent component analysis. We illustrate the proposed method on synthetic data.

PDF Web [BibTex]

PDF Web [BibTex]


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Additive Gaussian Processes

Duvenaud, D., Nickisch, H., Rasmussen, C.

In Advances in Neural Information Processing Systems 24, pages: 226-234, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract
We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.

PDF Web [BibTex]

PDF Web [BibTex]


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k-NN Regression Adapts to Local Intrinsic Dimension

Kpotufe, S.

In Advances in Neural Information Processing Systems 24, pages: 729-737, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract
Many nonparametric regressors were recently shown to converge at rates that depend only on the intrinsic dimension of data. These regressors thus escape the curse of dimension when high-dimensional data has low intrinsic dimension (e.g. a manifold). We show that k-NN regression is also adaptive to intrinsic dimension. In particular our rates are local to a query x and depend only on the way masses of balls centered at x vary with radius. Furthermore, we show a simple way to choose k = k(x) locally at any x so as to nearly achieve the minimax rate at x in terms of the unknown intrinsic dimension in the vicinity of x. We also establish that the minimax rate does not depend on a particular choice of metric space or distribution, but rather that this minimax rate holds for any metric space and doubling measure.

PDF Web [BibTex]

PDF Web [BibTex]


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Fast Newton-type Methods for Total-Variation with Applications

Barbero, A., Sra, S.

In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, pages: 313-320, (Editors: L Getoor and T Scheffer), Omnipress, 28th International Conference on Machine Learning (ICML), 2011 (inproceedings)

[BibTex]

[BibTex]


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Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees

Gonzalez, J., Low, Y., Gretton, A., Guestrin, C.

In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Vol. 15, pages: 324-332, (Editors: G Gordon and D Dunson and M Dudík), JMLR, AISTATS, 2011 (inproceedings)

PDF [BibTex]

PDF [BibTex]


Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Gehler, P., Rother, C., Kiefel, M., Zhang, L., Schölkopf, B.

In Advances in Neural Information Processing Systems 24, pages: 765-773, (Editors: Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.), Curran Associates, Inc., Red Hook, NY, USA, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract
We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.

website + code pdf poster Project Page Project Page [BibTex]

website + code pdf poster Project Page Project Page [BibTex]

2004


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Attentional Modulation of Auditory Event-Related Potentials in a Brain-Computer Interface

Hill, J., Lal, T., Bierig, K., Birbaumer, N., Schölkopf, B.

In BioCAS04, (S3/5/INV- S3/17-20):4, IEEE Computer Society, Los Alamitos, CA, USA, 2004 IEEE International Workshop on Biomedical Circuits and Systems, December 2004 (inproceedings)

Abstract
Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user‘s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.

PDF Web DOI [BibTex]

2004

PDF Web DOI [BibTex]


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Modelling Spikes with Mixtures of Factor Analysers

Görür, D., Rasmussen, C., Tolias, A., Sinz, F., Logothetis, N.

In Pattern Recognition, pages: 391-398, LNCS 3175, (Editors: Rasmussen, C. E. , H.H. Bülthoff, B. Schölkopf, M.A. Giese), Springer, Berlin, Germany, 26th DAGM Symposium, September 2004 (inproceedings)

Abstract
Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging problem. We consider the spike sorting problem using a generative model,mixtures of factor analysers, which concurrently performs clustering and feature extraction. The most important advantage of this method is that it quantifies the certainty with which the spikes are classified. This can be used as a means for evaluating the quality of clustering and therefore spike isolation. Using this method, nearly simultaneously occurring spikes can also be modelled which is a hard task for many of the spike sorting methods. Furthermore, modelling the data with a generative model allows us to generate simulated data.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Learning Depth From Stereo

Sinz, F., Candela, J., BakIr, G., Rasmussen, C., Franz, M.

In 26th DAGM Symposium, pages: 245-252, LNCS 3175, (Editors: Rasmussen, C. E., H. H. Bülthoff, B. Schölkopf, M. A. Giese), Springer, Berlin, Germany, 26th DAGM Symposium, September 2004 (inproceedings)

Abstract
We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1.~The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2.~A generic machine learning approach where the mapping from image to spatial coordinates is directly approximated by a Gaussian Process regression. Our results show that the generic learning approach, in addition to simplifying the procedure of calibration, can lead to higher depth accuracies than classical calibration although no specific domain knowledge is used.

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Learning to Find Graph Pre-Images

BakIr, G., Zien, A., Tsuda, K.

In Pattern Recognition, pages: 253-261, (Editors: Rasmussen, C. E., H. H. Bülthoff, B. Schölkopf, M. A. Giese), Springer, Berlin, Germany, 26th DAGM Symposium, August 2004 (inproceedings)

Abstract
The recent development of graph kernel functions has made it possible to apply well-established machine learning methods to graphs. However, to allow for analyses that yield a graph as a result, it is necessary to solve the so-called pre-image problem: to reconstruct a graph from its feature space representation induced by the kernel. Here, we suggest a practical solution to this problem.

PostScript PDF DOI [BibTex]

PostScript PDF DOI [BibTex]


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Exponential Families for Conditional Random Fields

Altun, Y., Smola, A., Hofmann, T.

In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004), pages: 2-9, (Editors: Chickering, D.M. , J.Y. Halpern), Morgan Kaufmann, San Francisco, CA, USA, 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI), July 2004 (inproceedings)

Abstract
In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present efficient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited efficiently in the optimization process.

PDF Web [BibTex]

PDF Web [BibTex]


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PAC-Bayesian Generic Chaining

Audibert, J., Bousquet, O.

In Advances in Neural Information Processing Systems 16, pages: 1125-1132 , (Editors: Thrun, S., L.K. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
There exist many different generalization error bounds for classification. Each of these bounds contains an improvement over the others for certain situations. Our goal is to combine these different improvements into a single bound. In particular we combine the PAC-Bayes approach introduced by McAllester, which is interesting for averaging classifiers, with the optimal union bound provided by the generic chaining technique developed by Fernique and Talagrand. This combination is quite natural since the generic chaining is based on the notion of majorizing measures, which can be considered as priors on the set of classifiers, and such priors also arise in the PAC-bayesian setting.

PDF Web [BibTex]

PDF Web [BibTex]


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Prediction on Spike Data Using Kernel Algorithms

Eichhorn, J., Tolias, A., Zien, A., Kuss, M., Rasmussen, C., Weston, J., Logothetis, N., Schölkopf, B.

In Advances in Neural Information Processing Systems 16, pages: 1367-1374, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a population of simultaneously recorded neurons. We compare several ways of improving the coding of the input (i.e., the spike data) as well as of the output (i.e., the orientation), and report the results obtained using different kernel algorithms.

PDF Web [BibTex]

PDF Web [BibTex]


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Warped Gaussian Processes

Snelson, E., Rasmussen, CE., Ghahramani, Z.

In Advances in Neural Information Processing Systems 16, pages: 337-344, (Editors: Thrun, S., L.K. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP. This can be seen as including a preprocessing transformation as an integral part of the probabilistic modelling problem, rather than as an ad-hoc step. We demonstrate on several real regression problems that learning the transformation can lead to significantly better performance than using a regular GP, or a GP with a fixed transformation.

PDF Web [BibTex]

PDF Web [BibTex]


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Ranking on Data Manifolds

Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.

In Advances in neural information processing systems 16, pages: 169-176, (Editors: S Thrun and L Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
The Google search engine has enjoyed a huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a simple universal ranking algorithm for data lying in the Euclidean space, such as text or image data. The core idea of our method is to rank the data with respect to the intrinsic manifold structure collectively revealed by a great amount of data. Encouraging experimental results from synthetic, image, and text data illustrate the validity of our method.

PDF Web [BibTex]

PDF Web [BibTex]


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Gaussian Processes in Reinforcement Learning

Rasmussen, C., Kuss, M.

In Advances in Neural Information Processing Systems 16, pages: 751-759, (Editors: Thrun, S., L. K. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP model allows evaluation of the value function in closed form. The resulting policy iteration algorithm is demonstrated on a simple problem with a two dimensional state space. Further, we speculate that the intrinsic ability of GP models to characterise distributions of functions would allow the method to capture entire distributions over future values instead of merely their expectation, which has traditionally been the focus of much of reinforcement learning.

PDF Web [BibTex]

PDF Web [BibTex]


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Learning with Local and Global Consistency

Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 16, pages: 321-328, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

PDF Web [BibTex]

PDF Web [BibTex]


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Learning to Find Pre-Images

Bakir, G., Weston, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 16, pages: 449-456, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of input points mapped into the RKHS. We introduce a technique based on kernel principal component analysis and regression to reconstruct corresponding patterns in the input space (aka pre-images) and review its performance in several applications requiring the construction of pre-images. The introduced technique avoids difficult and/or unstable numerical optimization, is easy to implement and, unlike previous methods, permits the computation of pre-images in discrete input spaces.

PDF Web [BibTex]

PDF Web [BibTex]


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Measure Based Regularization

Bousquet, O., Chapelle, O., Hein, M.

In Advances in Neural Information Processing Systems 16, pages: 1221-1228, (Editors: Thrun, S., L. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We also propose practical implementations.

PDF Web [BibTex]

PDF Web [BibTex]


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Insights from Machine Learning Applied to Human Visual Classification

Graf, A., Wichmann, F.

In Advances in Neural Information Processing Systems 16, pages: 905-912, (Editors: Thrun, S., L. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
We attempt to understand visual classification in humans using both psychophysical and machine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classified the faces and their gender judgment, reaction time and confidence rating were recorded. Several hyperplane learning algorithms were used on the same classification task using the Principal Components of the texture and flowfield representation of the faces. The classification performance of the learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender estimated by the subjects. We then correlated the human responses to the distance of the stimuli to the separating hyperplane of the learning algorithms. Our results suggest that human classification can be modeled by some hyperplane algorithms in the feature space we used. For classification, the brain needs more processing for stimuli close to that hyperplane than for those further away.

PDF Web [BibTex]

PDF Web [BibTex]


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Image Construction by Linear Programming

Tsuda, K., Rätsch, G.

In Advances in Neural Information Processing Systems 16, pages: 57-64, (Editors: Thrun, S., L.K. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
A common way of image denoising is to project a noisy image to the subspace of admissible images made for instance by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion. We propose a new method to identify the noisy pixels by 1-norm penalization and update the identified pixels only. The identification and updating of noisy pixels are formulated as one linear program which can be solved efficiently. Especially, one can apply the ν-trick to directly specify the fraction of pixels to be reconstructed. Moreover, we extend the linear program to be able to exploit prior knowledge that occlusions often appear in contiguous blocks (e.g. sunglasses on faces). The basic idea is to penalize boundary points and interior points of the occluded area differently. We are able to show the ν-property also for this extended LP leading a method which is easy to use. Experimental results impressively demonstrate the power of our approach.

PDF Web [BibTex]

PDF Web [BibTex]


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Semi-Supervised Protein Classification using Cluster Kernels

Weston, J., Leslie, C., Zhou, D., Elisseeff, A., Noble, W.

In Advances in Neural Information Processing Systems 16, pages: 595-602, (Editors: Thrun, S., L.K. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

Abstract
A key issue in supervised protein classification is the representation of input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data --- examples with known 3D structures, organized into structural classes --- while in practice, unlabeled data is far more plentiful. In this work, we develop simple and scalable cluster kernel techniques for incorporating unlabeled data into the representation of protein sequences. We show that our methods greatly improve the classification performance of string kernels and outperform standard approaches for using unlabeled data, such as adding close homologs of the positive examples to the training data. We achieve equal or superior performance to previously presented cluster kernel methods while achieving far greater computational efficiency.

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Hebbian Algorithm for single-frame super-resolution

Kim, K., Franz, M., Schölkopf, B.

In Computer Vision - ECCV 2004, LNCS vol. 3024, pages: 135-149, (Editors: A Leonardis and H Bischof), Springer, Berlin, Germany, 8th European Conference on Computer Vision (ECCV), May 2004 (inproceedings)

Abstract
This paper presents a method for single-frame image super-resolution using an unsupervised learning technique. The required prior knowledge about the high-resolution images is obtained from Kernel Principal Component Analysis (KPCA). The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the {em Kernel Hebbian Algorithm}. By kernelizing the Generalized Hebbian Algorithm, one can iteratively estimate the Kernel Principal Components with only linear order memory complexity. The resulting super-resolution algorithm shows a comparable performance to the existing supervised methods on images containing faces and natural scenes.

PDF Web [BibTex]

PDF Web [BibTex]


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Unifying Colloborative and Content-Based Filtering.

Basilico, J., Hofmann, T.

In ACM International Conference Proceeding Series, pages: 65 , (Editors: Greiner, R. , D. Schuurmans), ACM Press, New York, USA, ICLM, 2004 (inproceedings)

Abstract
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.

PDF [BibTex]

PDF [BibTex]


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Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models

Dubey, A., Hwang, S., Rangel, C., Rasmussen, CE., Ghahramani, Z., Wild, DL.

In Pacific Symposium on Biocomputing 2004; Vol. 9, pages: 399-410, World Scientific Publishing, Singapore, Pacific Symposium on Biocomputing, 2004 (inproceedings)

Abstract
We describe a novel approach to the problem of automatically clustering protein sequences and discovering protein families, subfamilies etc., based on the thoery of infinite Gaussian mixture models. This method allows the data itself to dictate how many mixture components are required to model it, and provides a measure of the probability that two proteins belong to the same cluster. We illustrate our methods with application to three data sets: globin sequences, globin sequences with known tree-dimensional structures and G-pretein coupled receptor sequences. The consistency of the clusters indicate that that our methods is producing biologically meaningful results, which provide a very good indication of the underlying families and subfamilies. With the inclusion of secondary structure and residue solvent accessibility information, we obtain a classification of sequences of known structure which reflects and extends their SCOP classifications. A supplementary web site containing larger versions of the figures is available at http://public.kgi.edu/~wild/PSB04

PDF [BibTex]

PDF [BibTex]


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Efficient Approximations for Support Vector Machines in Object Detection

Kienzle, W., BakIr, G., Franz, M., Schölkopf, B.

In DAGM 2004, pages: 54-61, (Editors: CE Rasmussen and HH Bülthoff and B Schölkopf and MA Giese), Springer, Berlin, Germany, Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are building on an existing algorithm where the set of support vectors is replaced by a smaller so-called reduced set of synthetic points. Instead of finding the reduced set via unconstrained optimization, we impose a structural constraint on the synthetic vectors such that the resulting approximation can be evaluated via separable filters. Applications that require scanning an entire image can benefit from this representation: when using separable filters, the average computational complexity for evaluating a reduced set vector on a test patch of size (h x w) drops from O(hw) to O(h+w). We show experimental results on handwritten digits and face detection.

PDF [BibTex]

PDF [BibTex]


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Kernel Methods for Manifold Estimation

Schölkopf, B.

In Proceedings in Computational Statistics, pages: 441-452, (Editors: J Antoch), Physica-Verlag/Springer, Heidelberg, Germany, COMPSTAT, 2004 (inproceedings)

[BibTex]

[BibTex]


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A Regularization Framework for Learningfrom Graph Data

Zhou, D., Schölkopf, B.

In ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields, pages: 132-137, ICML, 2004 (inproceedings)

Abstract
The data in many real-world problems can be thought of as a graph, such as the web, co-author networks, and biological networks. We propose a general regularization framework on graphs, which is applicable to the classification, ranking, and link prediction problems. We also show that the method can be explained as lazy random walks. We evaluate the method on a number of experiments.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A kernel view of the dimensionality reduction of manifolds

Ham, J., Lee, D., Mika, S., Schölkopf, B.

In Proceedings of the Twenty-First International Conference on Machine Learning, pages: 369-376, (Editors: CE Brodley), ACM, New York, NY, USA, ICML, 2004, also appeared as MPI-TR 110 (inproceedings)

Abstract
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples.

PDF [BibTex]

PDF [BibTex]


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Protein Functional Class Prediction with a Combined Graph

Shin, H., Tsuda, K., Schölkopf, B.

In Proceedings of the Korean Data Mining Conference, pages: 200-219, Proceedings of the Korean Data Mining Conference, 2004 (inproceedings)

Abstract
In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as protein-protein interactions, genetic interactions, or co-participation in a protein complex, etc. Relying on similarities between nodes, each graph can be used independently for prediction of protein function. However, since different graphs contain partly independent and partly complementary information about the problem at hand, one can enhance the total information extracted by combining all graphs. In this paper, we propose a method for integrating multiple graphs within a framework of semi-supervised learning. The method alternates between minimizing the objective function with respect to network output and with respect to combining weights. We apply the method to the task of protein functional class prediction in yeast. The proposed method performs significantly better than the same algorithm trained on any single graph.

PDF [BibTex]

PDF [BibTex]


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Learning from Labeled and Unlabeled Data Using Random Walks

Zhou, D., Schölkopf, B.

In Pattern Recognition, Proceedings of the 26th DAGM Symposium, pages: 237-244, (Editors: Rasmussen, C.E., H.H. Bülthoff, M.A. Giese and B. Schölkopf), Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classifier which uses the unlabeled data information in some way and has higher accuracy than the classifiers which use the labeled data only. Recently we proposed a simple algorithm, which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods. In this paper we further investigate the algorithm using random walks and spectral graph theory, which shed light on the key steps in this algorithm.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Multivariate Regression via Stiefel Manifold Constraints

BakIr, G., Gretton, A., Franz, M., Schölkopf, B.

In Lecture Notes in Computer Science, Vol. 3175, pages: 262-269, (Editors: CE Rasmussen and HH Bülthoff and B Schölkopf and MA Giese), Springer, Berlin, Germany, Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered independently. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss function that we specify, subject to a rank constraint. A major advantage of this approach is that the loss is no longer chosen according to the algorithmic requirements, but can be tailored to the characteristics of the task at hand; the features will then be optimal with respect to this objective, and dependence between the outputs can be exploited.

PostScript [BibTex]

PostScript [BibTex]


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Implicit estimation of Wiener series

Franz, M., Schölkopf, B.

In Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop, pages: 735-744, (Editors: A Barros and J Principe and J Larsen and T Adali and S Douglas), IEEE, New York, Machine Learning for Signal Processing XIV, Proc. IEEE Signal Processing Society Workshop, 2004 (inproceedings)

Abstract
The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method based on regression in a reproducing kernel Hilbert space that alleviates these problems. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Hilbertian Metrics on Probability Measures and their Application in SVM’s

Hein, H., Lal, T., Bousquet, O.

In Pattern Recognition, Proceedings of th 26th DAGM Symposium, 3175, pages: 270-277, Lecture Notes in Computer Science, (Editors: Rasmussen, C. E., H. H. Bülthoff, M. Giese and B. Schölkopf), Pattern Recognition, Proceedings of th 26th DAGM Symposium, 2004 (inproceedings)

Abstract
The goal of this article is to investigate the field of Hilbertian metrics on probability measures. Since they are very versatile and can therefore be applied in various problems they are of great interest in kernel methods. Quit recently Tops{o}e and Fuglede introduced a family of Hilbertian metrics on probability measures. We give basic properties of the Hilbertian metrics of this family and other used metrics in the literature. Then we propose an extension of the considered metrics which incorporates structural information of the probability space into the Hilbertian metric. Finally we compare all proposed metrics in an image and text classification problem using histogram data.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Gasussian process model based predictive control

Kocijan, J., Murray-Smith, R., Rasmussen, CE., Girard, A.

In Proceedings of the ACC 2004, pages: 2214-2219, Proceedings of the ACC, 2004 (inproceedings)

Abstract
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identi cation of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coef cients to be optimised. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A New Variational Framework for Rigid-Body Alignment

Kato, T., Tsuda, K., Tomii, K., Asai, K.

In Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), pages: 171-179, (Editors: Fred, A.,T. Caelli, R.P.W. Duin, A. Campilho and D. de Ridder), Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR) and Statistical Pattern Recognition (SPR), 2004 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Statistical Learning with Similarity and Dissimilarity Functions

von Luxburg, U.

pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Semi-supervised kernel regression using whitened function classes

Franz, M., Kwon, Y., Rasmussen, C., Schölkopf, B.

In Pattern Recognition, Proceedings of the 26th DAGM Symposium, Lecture Notes in Computer Science, Vol. 3175, LNCS 3175, pages: 18-26, (Editors: CE Rasmussen and HH Bülthoff and MA Giese and B Schölkopf), Springer, Berlin, Gerrmany, 26th DAGM Symposium, 2004 (inproceedings)

Abstract
The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O.

In Learning Theory and Kernel Machines, pages: 72-86, (Editors: Schölkopf, B. and Warmuth, M. K.), Springer, Heidelberg, Germany, 16. Annual Conference on Computational Learning Theory / COLT Kernel, 2004 (inproceedings)

Abstract
In this article we construct a maximal margin classification algorithm for arbitrary metric spaces. At first we show that the Support Vector Machine (SVM) is a maximal margin algorithm for the class of metric spaces where the negative squared distance is conditionally positive definite (CPD). This means that the metric space can be isometrically embedded into a Hilbert space, where one performs linear maximal margin separation. We will show that the solution only depends on the metric, but not on the kernel. Following the framework we develop for the SVM, we construct an algorithm for maximal margin classification in arbitrary metric spaces. The main difference compared with SVM is that we no longer embed isometrically into a Hilbert space, but a Banach space. We further give an estimate of the capacity of the function class involved in this algorithm via Rademacher averages. We recover an algorithm of Graepel et al. [6].

PDF PostScript PDF DOI [BibTex]

PDF PostScript PDF DOI [BibTex]


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On the Convergence of Spectral Clustering on Random Samples: The Normalized Case

von Luxburg, U., Bousquet, O., Belkin, M.

In Proceedings of the 17th Annual Conference on Learning Theory, pages: 457-471, Proceedings of the 17th Annual Conference on Learning Theory, 2004 (inproceedings)

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Classification and Feature Extraction in Man and Machine

Graf, AAB.

Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

[BibTex]

[BibTex]

2002


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Gender Classification of Human Faces

Graf, A., Wichmann, F.

In Biologically Motivated Computer Vision, pages: 1-18, (Editors: Bülthoff, H. H., S.W. Lee, T. A. Poggio and C. Wallraven), Springer, Berlin, Germany, Second International Workshop on Biologically Motivated Computer Vision (BMCV), November 2002 (inproceedings)

Abstract
This paper addresses the issue of combining pre-processing methods—dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)—with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.

PDF PDF DOI [BibTex]

2002

PDF PDF DOI [BibTex]


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Insect-Inspired Estimation of Self-Motion

Franz, MO., Chahl, JS.

In Biologically Motivated Computer Vision, (2525):171-180, LNCS, (Editors: Bülthoff, H.H. , S.W. Lee, T.A. Poggio, C. Wallraven), Springer, Berlin, Germany, Second International Workshop on Biologically Motivated Computer Vision (BMCV), November 2002 (inproceedings)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an optimal linear estimator incorporating prior knowledge about the environment. The optimal estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]