Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

460 results (View BibTeX file of all listed publications)

2006

MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models

ICML Workshop on Learning with Nonparametric Bayesian Methods, June 2006 (talk)

Abstract

We compare the predictive accuracy of the Dirichlet Process Gaussian mixture models using conjugate and conditionally conjugate priors and show that better density models result from using the wider class of priors. We explore several MCMC schemes exploiting conditional conjugacy and show their computational merits on several multidimensional density estimation problems.

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2006

Rasmussen, C., Görür, D. MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models ICML Workshop on Learning with Nonparametric Bayesian Methods, June 2006 (talk)

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Unifying Divergence Minimization and Statistical Inference Via Convex Duality

Altun, Y., Smola, A.

In Learning Theory, pages: 139-153, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory (COLT), June 2006 (inproceedings)

Abstract

In this paper we unify divergence minimization and statistical inference by means of convex duality. In the process of doing so, we prove that the dual of approximate maximum entropy estimation is maximum a posteriori estimation as a special case. Moreover, our treatment leads to stability and convergence bounds for many statistical learning problems. Finally, we show how an algorithm by Zhang can be used to solve this class of optimization problems efficiently.

Web DOI [BibTex]

Altun, Y., Smola, A. Unifying Divergence Minimization and Statistical Inference Via Convex Duality In Learning Theory, pages: 139-153, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory (COLT), June 2006 (inproceedings)

Web DOI [BibTex]

Trading Convexity for Scalability

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

In ICML 2006, pages: 201-208, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract

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

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Collobert, R., Sinz, F., Weston, J., Bottou, L. Trading Convexity for Scalability In ICML 2006, pages: 201-208, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

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Personalized handwriting recognition via biased regularization

Kienzle, W., Chellapilla, K.

In ICML 2006, pages: 457-464, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract

We present a new approach to personalized handwriting recognition. The problem, also known as writer adaptation, consists of converting a generic (user-independent) recognizer into a personalized (user-dependent) one, which has an improved recognition rate for a particular user. The adaptation step usually involves user-specific samples, which leads to the fundamental question of how to fuse this new information with that captured by the generic recognizer. We propose adapting the recognizer by minimizing a regularized risk functional (a modified SVM) where the prior knowledge from the generic recognizer enters through a modified regularization term. The result is a simple personalization framework with very good practical properties. Experiments on a 100 class real-world data set show that the number of errors can be reduced by over 40% with as few as five user samples per character.

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Kienzle, W., Chellapilla, K. Personalized handwriting recognition via biased regularization In ICML 2006, pages: 457-464, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

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Sampling for non-conjugate infinite latent feature models

Görür, D., Rasmussen, C.

(Editors: Bernardo, J. M.), 8th Valencia International Meeting on Bayesian Statistics (ISBA), June 2006 (talk)

Abstract

Latent variable models are powerful tools to model the underlying structure in data. Infinite latent variable models can be defined using Bayesian nonparametrics. Dirichlet process (DP) models constitute an example of infinite latent class models in which each object is assumed to belong to one of the, mutually exclusive, infinitely many classes. Recently, the Indian buffet process (IBP) has been defined as an extension of the DP. IBP is a distribution over sparse binary matrices with infinitely many columns which can be used as a distribution for non-exclusive features. Inference using Markov chain Monte Carlo (MCMC) in conjugate IBP models has been previously described, however requiring conjugacy restricts the use of IBP. We describe an MCMC algorithm for non-conjugate IBP models. Modelling the choice behaviour is an important topic in psychology, economics and related fields. Elimination by Aspects (EBA) is a choice model that assumes each alternative has latent features with associated weights that lead to the observed choice outcomes. We formulate a non-parametric version of EBA by using IBP as the prior over the latent binary features. We infer the features of objects that lead to the choice data by using our sampling scheme for inference.

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Görür, D., Rasmussen, C. Sampling for non-conjugate infinite latent feature models (Editors: Bernardo, J. M.), 8th Valencia International Meeting on Bayesian Statistics (ISBA), June 2006 (talk)

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Deterministic annealing for semi-supervised kernel machines

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

In ICML 2006, pages: 841-848, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract

An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat the unknown labels as additional optimization variables. For margin-based loss functions, one can view this approach as attempting to learn low-density separators. However, this is a hard optimization problem to solve in typical semi-supervised settings where unlabeled data is abundant. The popular Transductive SVM algorithm is a label-switching-retraining procedure that is known to be susceptible to local minima. In this paper, we present a global optimization framework for semi-supervised Kernel machines where an easier problem is parametrically deformed to the original hard problem and minimizers are smoothly tracked. Our approach is motivated from deterministic annealing techniques and involves a sequence of convex optimization problems that are exactly and efficiently solved. We present empirical results on several synthetic and real world datasets that demonstrate the effectiveness of our approach.

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Sindhwani, V., Keerthi, S., Chapelle, O. Deterministic annealing for semi-supervised kernel machines In ICML 2006, pages: 841-848, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

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Clustering Graphs by Weighted Substructure Mining

Tsuda, K., Kudo, T.

In ICML 2006, pages: 953-960, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract

Graph data is getting increasingly popular in, e.g., bioinformatics and text processing. 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 subgraphs, the dimensionality gets too large for usual statistical methods. We propose an efficient method for learning a binomial mixture model in this feature space. Combining the $ell_1$ regularizer and the data structure called DFS code tree, the MAP estimate of non-zero parameters are computed efficiently by means of the EM algorithm. Our method is applied to the clustering of RNA graphs, and is compared favorably with graph kernels and the spectral graph distance.

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Tsuda, K., Kudo, T. Clustering Graphs by Weighted Substructure Mining In ICML 2006, pages: 953-960, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

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A Choice Model with Infinitely Many Latent Features

Görür, D., Jäkel, F., Rasmussen, C.

In ICML 2006, pages: 361-368, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract

Elimination by aspects (EBA) is a probabilistic choice model describing how humans decide between several options. The options from which the choice is made are characterized by binary features and associated weights. For instance, when choosing which mobile phone to buy the features to consider may be: long lasting battery, color screen, etc. Existing methods for inferring the parameters of the model assume pre-specified features. However, the features that lead to the observed choices are not always known. Here, we present a non-parametric Bayesian model to infer the features of the options and the corresponding weights from choice data. We use the Indian buffet process (IBP) as a prior over the features. Inference using Markov chain Monte Carlo (MCMC) in conjugate IBP models has been previously described. The main contribution of this paper is an MCMC algorithm for the EBA model that can also be used in inference for other non-conjugate IBP models---this may broaden the use of IBP priors considerably.

PostScript PDF Web DOI [BibTex]

Görür, D., Jäkel, F., Rasmussen, C. A Choice Model with Infinitely Many Latent Features In ICML 2006, pages: 361-368, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

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Learning High-Order MRF Priors of Color Images

McAuley, J., Caetano, T., Smola, A., Franz, MO.

In ICML 2006, pages: 617-624, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract

In this paper, we use large neighborhood Markov random fields to learn rich prior models of color images. Our approach extends the monochromatic Fields of Experts model (Roth and Blackwell, 2005) to color images. In the Fields of Experts model, the curse of dimensionality due to very large clique sizes is circumvented by parameterizing the potential functions according to a product of experts. We introduce several simplifications of the original approach by Roth and Black which allow us to cope with the increased clique size (typically 3x3x3 or 5x5x3 pixels) of color images. Experimental results are presented for image denoising which evidence improvements over state-of-the-art monochromatic image priors.

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McAuley, J., Caetano, T., Smola, A., Franz, MO. Learning High-Order MRF Priors of Color Images In ICML 2006, pages: 617-624, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

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Inference with the Universum

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

In ICML 2006, pages: 1009-1016, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract

WIn this paper we study a new framework introduced by Vapnik (1998) and Vapnik (2006) that is an alternative capacity concept to the large margin approach. In the particular case of binary classification, we are given a set of labeled examples, and a collection of "non-examples" that do not belong to either class of interest. This collection, called the Universum, allows one to encode prior knowledge by representing meaningful concepts in the same domain as the problem at hand. We describe an algorithm to leverage the Universum by maximizing the number of observed contradictions, and show experimentally that this approach delivers accuracy improvements over using labeled data alone.

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Weston, J., Collobert, R., Sinz, F., Bottou, L., Vapnik, V. Inference with the Universum In ICML 2006, pages: 1009-1016, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

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The pedestal effect is caused by off-frequency looking, not nonlinear transduction or contrast gain-control

Wichmann, F., Henning, B.

Journal of Vision, 6(6):194, 6th Annual Meeting of the Vision Sciences Society (VSS), June 2006 (poster)

Abstract

The pedestal or dipper effect is the large improvement in the detectabilty of a sinusoidal grating observed when the signal is added to a pedestal or masking grating having the signal‘s spatial frequency, orientation, and phase. The effect is largest with pedestal contrasts just above the ‘threshold‘ in the absence of a pedestal. We measured the pedestal effect in both broadband and notched masking noise---noise from which a 1.5- octave band centered on the signal and pedestal frequency had been removed. The pedestal effect persists in broadband noise, but almost disappears with notched noise. The spatial-frequency components of the notched noise that lie above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies that are different from that of the signal and pedestal. Thus the pedestal or dipper effect is not a characteristic of individual spatial-frequency tuned channels.

Web DOI [BibTex]

Wichmann, F., Henning, B. The pedestal effect is caused by off-frequency looking, not nonlinear transduction or contrast gain-control Journal of Vision, 6(6):194, 6th Annual Meeting of the Vision Sciences Society (VSS), June 2006 (poster)

Web DOI [BibTex]

Classifying EEG and ECoG Signals without Subject Training for Fast BCI Implementation: Comparison of Non-Paralysed and Completely Paralysed Subjects

Hill, N., Lal, T., Schröder, M., Hinterberger, T., Wilhelm, B., Nijboer, F., Mochty, U., Widman, G., Elger, C., Schölkopf, B., Kübler, A., Birbaumer, N.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2):183-186, June 2006 (article)

Abstract

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of EEG or ECoG signals for each subject. We apply the same experimental and analytical methods to 11 non-paralysed subjects (8 EEG, 3 ECoG), and to 5 paralysed subjects (4 EEG, 1 ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the non-paralysed subjects, it proved impossible to classify the signals obtained from the paralysed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

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Hill, N., Lal, T., Schröder, M., Hinterberger, T., Wilhelm, B., Nijboer, F., Mochty, U., Widman, G., Elger, C., Schölkopf, B., Kübler, A., Birbaumer, N. Classifying EEG and ECoG Signals without Subject Training for Fast BCI Implementation: Comparison of Non-Paralysed and Completely Paralysed Subjects IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2):183-186, June 2006 (article)

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Object Classification using Local Image Features

Nowozin, S.

Biologische Kybernetik, Technical University of Berlin, Berlin, Germany, May 2006 (diplomathesis)

Abstract

Object classification in digital images remains one of the most challenging tasks in computer vision. Advances in the last decade have produced methods to repeatably extract and describe characteristic local features in natural images. In order to apply machine learning techniques in computer vision systems, a representation based on these features is needed. A set of local features is the most popular representation and often used in conjunction with Support Vector Machines for classification problems. In this work, we examine current approaches based on set representations and identify their shortcomings. To overcome these shortcomings, we argue for extending the set representation into a graph representation, encoding more relevant information. Attributes associated with the edges of the graph encode the geometric relationships between individual features by making use of the meta data of each feature, such as the position, scale, orientation and shape of the feature region. At the same time all invariances provided by the original feature extraction method are retained. To validate the novel approach, we use a standard subset of the ETH-80 classification benchmark.

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Nowozin, S. Object Classification using Local Image Features Biologische Kybernetik, Technical University of Berlin, Berlin, Germany, May 2006 (diplomathesis)

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SCARNA: Fast and Accurate Structural Alignment of RNA Sequences by Matching Fixed-Length Stem Fragments

Tabei, Y., Tsuda, K., Kin, T., Asai, K.

Bioinformatics, 22(14):1723-1729, May 2006 (article)

Abstract

The functions of non-coding RNAs are strongly related to their secondary structures, but it is known that a secondary structure prediction of a single sequence is not reliable. Therefore, we have to collect similar RNA sequences with a common secondary structure for the analyses of a new non-coding RNA without knowing the exact secondary structure itself. Therefore, the sequence comparison in searching similar RNAs should consider not only their sequence similarities but their potential secondary structures. Sankoff&lsquo;s algorithm predicts the common secondary structures of the sequences, but it is computationally too expensive to apply to large-scale analyses. Because we often want to compare a large number of cDNA sequences or to search similar RNAs in the whole genome sequences, much faster algorithms are required. We propose a new method of comparing RNA sequences based on the structural alignments of the fixed-length fragments of the stem candidates. The implemented software, SCARNA (Stem Candidate Aligner for RNAs), is fast enough to apply to the long sequences in the large-scale analyses. The accuracy of the alignments is better or comparable to the much slower existing algorithms.

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Tabei, Y., Tsuda, K., Kin, T., Asai, K. SCARNA: Fast and Accurate Structural Alignment of RNA Sequences by Matching Fixed-Length Stem Fragments Bioinformatics, 22(14):1723-1729, May 2006 (article)

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Statistical Convergence of Kernel CCA

Fukumizu, K., Bach, F., Gretton, A.

In Advances in neural information processing systems 18, pages: 387-394, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract

While kernel canonical correlation analysis (kernel CCA) has been applied in many problems, the asymptotic convergence of the functions estimated from a finite sample to the true functions has not yet been established. This paper gives a rigorous proof of the statistical convergence of kernel CCA and a related method (NOCCO), which provides a theoretical justification for these methods. The result also gives a sufficient condition on the decay of the regularization coefficient in the methods to ensure convergence.

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Fukumizu, K., Bach, F., Gretton, A. Statistical Convergence of Kernel CCA In Advances in neural information processing systems 18, pages: 387-394, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

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Response Modeling with Support Vector Machines

Shin, H., Cho, S.

Expert Systems with Applications, 30(4):746-760, May 2006 (article)

Abstract

Support Vector Machine (SVM) employs Structural Risk minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing,h owever,one has to deal with the practical difficulties: large training data,class imbalance and binary SVM output. This paper proposes ways to alleviate or solve the addressed difficulties through informative sampling,u se of different costs for different classes, and use of distance to decision boundary. This paper also provides various evaluation measures for response models in terms of accuracies,lift chart analysis and computational efficiency.

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Shin, H., Cho, S. Response Modeling with Support Vector Machines Expert Systems with Applications, 30(4):746-760, May 2006 (article)

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Maximum Margin Semi-Supervised Learning for Structured Variables

Altun, Y., McAllester, D., Belkin, M.

In Advances in neural information processing systems 18, pages: 33-40, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract

Many real-world classification problems involve the prediction of multiple inter-dependent variables forming some structural dependency. Recent progress in machine learning has mainly focused on supervised classification of such structured variables. In this paper, we investigate structured classification in a semi-supervised setting. We present a discriminative approach that utilizes the intrinsic geometry of input patterns revealed by unlabeled data points and we derive a maximum-margin formulation of semi-supervised learning for structured variables. Unlike transductive algorithms, our formulation naturally extends to new test points.

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Altun, Y., McAllester, D., Belkin, M. Maximum Margin Semi-Supervised Learning for Structured Variables In Advances in neural information processing systems 18, pages: 33-40, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

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Generalized Nonnegative Matrix Approximations with Bregman Divergences

Dhillon, I., Sra, S.

In Advances in neural information processing systems 18, pages: 283-290, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract

Nonnegative matrix approximation (NNMA) is a recent technique for dimensionality reduction and data analysis that yields a parts based, sparse nonnegative representation for nonnegative input data. NNMA has found a wide variety of applications, including text analysis, document clustering, face/image recognition, language modeling, speech processing and many others. Despite these numerous applications, the algorithmic development for computing the NNMA factors has been relatively efficient. This paper makes algorithmic progress by modeling and solving (using multiplicative updates) new generalized NNMA problems that minimize Bregman divergences between the input matrix and its lowrank approximation. The multiplicative update formulae in the pioneering work by Lee and Seung [11] arise as a special case of our algorithms. In addition, the paper shows how to use penalty functions for incorporating constraints other than nonnegativity into the problem. Further, some interesting extensions to the use of "link" functions for modeling nonlinear relationships are also discussed.

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Dhillon, I., Sra, S. Generalized Nonnegative Matrix Approximations with Bregman Divergences In Advances in neural information processing systems 18, pages: 283-290, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

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Fast Gaussian Process Regression using KD-Trees

Shen, Y., Ng, A., Seeger, M.

In Advances in neural information processing systems 18, pages: 1225-1232, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract

The computation required for Gaussian process regression with n training examples is about O(n3) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.

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Shen, Y., Ng, A., Seeger, M. Fast Gaussian Process Regression using KD-Trees In Advances in neural information processing systems 18, pages: 1225-1232, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

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Products of "Edge-perts"

Gehler, PV., Welling, M.

In Advances in neural information processing systems 18, pages: 419-426, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract

Images represent an important and abundant source of data. Understanding their statistical structure has important applications such as image compression and restoration. In this paper we propose a particular kind of probabilistic model, dubbed the “products of edge-perts model” to describe the structure of wavelet transformed images. We develop a practical denoising algorithm based on a single edge-pert and show state-ofthe-art denoising performance on benchmark images.

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Gehler, PV., Welling, M. Products of "Edge-perts" In Advances in neural information processing systems 18, pages: 419-426, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

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Assessing Approximations for Gaussian Process Classification

Kuss, M., Rasmussen, C.

In Advances in neural information processing systems 18, pages: 699-706, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract

Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplace&amp;amp;amp;amp;amp;amp;amp;lsquo;s method and Expectation Propagation (EP) focusing on marginal likelihood estimates and predictive performance. We explain theoretically and corroborate empirically that EP is superior to Laplace. We also compare to a sophisticated MCMC scheme and show that EP is surprisingly accurate.

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Kuss, M., Rasmussen, C. Assessing Approximations for Gaussian Process Classification In Advances in neural information processing systems 18, pages: 699-706, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

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Optimizing amino acid substitution matrices with a local alignment kernel

Saigo, H., Vert, J., Akutsu, T.

BMC Bioinformatics, 7(246):1-12, May 2006 (article)

Abstract

Background Detecting remote homologies by direct comparison of protein sequences remains a challenging task. We had previously developed a similarity score between sequences, called a local alignment kernel, that exhibits good performance for this task in combination with a support vector machine. The local alignment kernel depends on an amino acid substitution matrix. Since commonly used BLOSUM or PAM matrices for scoring amino acid matches have been optimized to be used in combination with the Smith-Waterman algorithm, the matrices optimal for the local alignment kernel can be different. Results Contrary to the local alignment score computed by the Smith-Waterman algorithm, the local alignment kernel is differentiable with respect to the amino acid substitution and its derivative can be computed efficiently by dynamic programming. We optimized the substitution matrix by classical gradient descent by setting an objective function that measures how well the local alignment kernel discriminates homologs from non-homologs in the COG database. The local alignment kernel exhibits better performance when it uses the matrices and gap parameters optimized by this procedure than when it uses the matrices optimized for the Smith-Waterman algorithm. Furthermore, the matrices and gap parameters optimized for the local alignment kernel can also be used successfully by the Smith-Waterman algorithm. Conclusion This optimization procedure leads to useful substitution matrices, both for the local alignment kernel and the Smith-Waterman algorithm. The best performance for homology detection is obtained by the local alignment kernel.

Web DOI [BibTex]

Saigo, H., Vert, J., Akutsu, T. Optimizing amino acid substitution matrices with a local alignment kernel BMC Bioinformatics, 7(246):1-12, May 2006 (article)

Web DOI [BibTex]

Worst-Case Bounds for Gaussian Process Models

Kakade, S., Seeger, M., Foster, D.

In Advances in neural information processing systems 18, pages: 619-626, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract

We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabilistic assumptions about the generation of the data are made. We consider models which use a Gaussian process prior (over the space of all functions) and provide bounds on the regret (under the log loss) for commonly used non-parametric Bayesian algorithms - including Gaussian regression and logistic regression - which show how these algorithms can perform favorably under rather general conditions. These bounds explicitly handle the infinite dimensionality of these non-parametric classes in a natural way. We also make formal connections to the minimax and emph{minimum description length} (MDL) framework. Here, we show precisely how Bayesian Gaussian regression is a minimax strategy.

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Kakade, S., Seeger, M., Foster, D. Worst-Case Bounds for Gaussian Process Models In Advances in neural information processing systems 18, pages: 619-626, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

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Nonnegative Matrix Approximation: Algorithms and Applications

Sra, S., Dhillon, I.

Univ. of Texas, Austin, May 2006 (techreport)

[BibTex]

Sra, S., Dhillon, I. Nonnegative Matrix Approximation: Algorithms and Applications Univ. of Texas, Austin, May 2006 (techreport)

[BibTex]

Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference

Weiss, Y., Schölkopf, B., Platt, J.

Proceedings of the 19th Annual Conference on Neural Information Processing Systems (NIPS 2005), pages: 1676, MIT Press, Cambridge, MA, USA, 19th Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (proceedings)

Abstract

The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2005 meeting, held in Vancouver.

Web [BibTex]

Weiss, Y., Schölkopf, B., Platt, J. Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference Proceedings of the 19th Annual Conference on Neural Information Processing Systems (NIPS 2005), pages: 1676, MIT Press, Cambridge, MA, USA, 19th Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (proceedings)

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Row-Action Methods for Compressed Sensing

Sra, S., Tropp, J.

In ICASSP 2006, pages: 868-871, IEEE Operations Center, Piscataway, NJ, USA, IEEE International Conference on Acoustics, Speech and Signal Processing, May 2006 (inproceedings)

Abstract

Compressed Sensing uses a small number of random, linear measurements to acquire a sparse signal. Nonlinear algorithms, such as l1 minimization, are used to reconstruct the signal from the measured data. This paper proposes rowaction methods as a computational approach to solving the l1 optimization problem. This paper presents a specific rowaction method and provides extensive empirical evidence that it is an effective technique for signal reconstruction. This approach offers several advantages over interior-point methods, including minimal storage and computational requirements, scalability, and robustness.

Web DOI [BibTex]

Sra, S., Tropp, J. Row-Action Methods for Compressed Sensing In ICASSP 2006, pages: 868-871, IEEE Operations Center, Piscataway, NJ, USA, IEEE International Conference on Acoustics, Speech and Signal Processing, May 2006 (inproceedings)

Web DOI [BibTex]

Learning an Interest Operator from Human Eye Movements

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

In CVPWR 2006, pages: page 24, (Editors: C Schmid and S Soatto and C Tomasi), IEEE Computer Society, Los Alamitos, CA, USA, 2006 Conference on Computer Vision and Pattern Recognition Workshop, April 2006 (inproceedings)

Abstract

We present an approach for designing interest operators that are based on human eye movement statistics. In contrast to existing methods which use hand-crafted saliency measures, we use machine learning methods to infer an interest operator directly from eye movement data. That way, the operator provides a measure of biologically plausible interestingness. We describe the data collection, training, and evaluation process, and show that our learned saliency measure significantly accounts for human eye movements. Furthermore, we illustrate connections to existing interest operators, and present a multi-scale interest point detector based on the learned function.

PDF Web DOI [BibTex]

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M. Learning an Interest Operator from Human Eye Movements In CVPWR 2006, pages: page 24, (Editors: C Schmid and S Soatto and C Tomasi), IEEE Computer Society, Los Alamitos, CA, USA, 2006 Conference on Computer Vision and Pattern Recognition Workshop, April 2006 (inproceedings)

PDF Web DOI [BibTex]

Evaluating Predictive Uncertainty Challenge

Quinonero Candela, J., Rasmussen, C., Sinz, F., Bousquet, O., Schölkopf, B.

In Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, pages: 1-27, (Editors: J Quiñonero Candela and I Dagan and B Magnini and F d’Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop (MLCW), April 2006 (inproceedings)

Abstract

This Chapter presents the PASCAL Evaluating Predictive Uncertainty Challenge, introduces the contributed Chapters by the participants who obtained outstanding results, and provides a discussion with some lessons to be learnt. The Challenge was set up to evaluate the ability of Machine Learning algorithms to provide good “probabilistic predictions”, rather than just the usual “point predictions” with no measure of uncertainty, in regression and classification problems. Parti-cipants had to compete on a number of regression and classification tasks, and were evaluated by both traditional losses that only take into account point predictions and losses we proposed that evaluate the quality of the probabilistic predictions.

PDF Web DOI [BibTex]

Quinonero Candela, J., Rasmussen, C., Sinz, F., Bousquet, O., Schölkopf, B. Evaluating Predictive Uncertainty Challenge In Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, pages: 1-27, (Editors: J Quiñonero Candela and I Dagan and B Magnini and F d’Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop (MLCW), April 2006 (inproceedings)

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The Effect of Artifacts on Dependence Measurement in fMRI

Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N.

Magnetic Resonance Imaging, 24(4):401-409, April 2006 (article)

PDF Web DOI [BibTex]

Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N. The Effect of Artifacts on Dependence Measurement in fMRI Magnetic Resonance Imaging, 24(4):401-409, April 2006 (article)

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An Automated Combination of Sequence Motif Kernels for Predicting Protein Subcellular Localization

Zien, A., Ong, C.

(146), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006 (techreport)

Abstract

Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. We propose an elegant and fully automated approach to building a prediction system for protein subcellular localization. We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We further propose a multiclass support vector machine method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we generalize our method to optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets.

PDF Web [BibTex]

Zien, A., Ong, C. An Automated Combination of Sequence Motif Kernels for Predicting Protein Subcellular Localization (146), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006 (techreport)

PDF Web [BibTex]

Einer für viele: Ein Linux-PC bedient mehrere Arbeitsplätze

Renner, M., Stark, S.

c‘t, 2006(10):228-235, April 2006 (article)

Abstract

Ein moderner PC ist rechenstark genug, um mehrere Anwender gleichzeitig zu bedienen; und Linux als Multi-User-System ist von Hause aus darauf vorbereitet, mehrere gleichzeitig angemeldete Benutzer mit einem eigenen grafischen Desktop zu versorgen. Mit einem Kernelpatch und ein wenig Bastelei lassen sich an einen Linux-PC sogar mehrere unabh{\"a}ngige Monitore, Tastaturen und M{\"a}use anschließen.

Web [BibTex]

Renner, M., Stark, S. Einer für viele: Ein Linux-PC bedient mehrere Arbeitsplätze c‘t, 2006(10):228-235, April 2006 (article)

Web [BibTex]

Phase noise and the classification of natural images

Wichmann, F., Braun, D., Gegenfurtner, K.

Vision Research, 46(8-9):1520-1529, April 2006 (article)

Abstract

We measured the effect of global phase manipulations on a rapid animal categorization task. The Fourier spectra of our images of natural scenes were manipulated by adding zero-mean random phase noise at all spatial frequencies. The phase noise was the independent variable, uniformly and symmetrically distributed between 0 degree and ±180 degrees. Subjects were remarkably resistant to phase noise. Even with ±120 degree phase noise subjects were still performing at 75% correct. The high resistance of the subjects animal categorization rate to phase noise suggests that the visual system is highly robust to such random image changes. The proportion of correct answers closely followed the correlation between original and the phase noise-distorted images. Animal detection rate was higher when the same task was performed with contrast reduced versions of the same natural images, at contrasts where the contrast reduction mimicked that resulting from our phase randomization. Since the subjects categorization rate was better in the contrast experiment, reduction of local contrast alone cannot explain the performance in the phase noise experiment. This result obtained with natural images differs from those obtained for simple sinusoidal stimuli were performance changes due to phase changes are attributed to local contrast changes only. Thus the global phasechange accompanying disruption of image structure such as edges and object boundaries at different spatial scales reduces object classification over and above the performance deficit resulting from reducing contrast. Additional colour information improves the categorization performance by 2 %.

PDF Web DOI [BibTex]

Wichmann, F., Braun, D., Gegenfurtner, K. Phase noise and the classification of natural images Vision Research, 46(8-9):1520-1529, April 2006 (article)

PDF Web DOI [BibTex]

Training a Support Vector Machine in the Primal

Chapelle, O.

(147), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006, The version in the "Large Scale Kernel Machines" book is more up to date. (techreport)

Abstract

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

PDF [BibTex]

Chapelle, O. Training a Support Vector Machine in the Primal (147), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006, The version in the "Large Scale Kernel Machines" book is more up to date. (techreport)

PDF [BibTex]

Kernel PCA for Image Compression

Huhle, B.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, Germany, April 2006 (diplomathesis)

PDF [BibTex]

Huhle, B. Kernel PCA for Image Compression Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, Germany, April 2006 (diplomathesis)

PDF [BibTex]

Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants

Danziger, S., Swamidass, S., Zeng, J., Dearth, L., Lu, Q., Cheng, J., Cheng, J., Hoang, V., Saigo, H., Luo, R., Baldi, P., Brachmann, R., Lathrop, R.

IEEE Transactions on Computational Biology and Bioinformatics, 3(2):114-125, April 2006 (article)

Abstract

Many biomedical problems relate to mutant functional properties across a sequence space of interest, e.g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three (24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early (overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R.H.L. for computation and R.K.B. for biology).

PDF DOI [BibTex]

Danziger, S., Swamidass, S., Zeng, J., Dearth, L., Lu, Q., Cheng, J., Cheng, J., Hoang, V., Saigo, H., Luo, R., Baldi, P., Brachmann, R., Lathrop, R. Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants IEEE Transactions on Computational Biology and Bioinformatics, 3(2):114-125, April 2006 (article)

PDF DOI [BibTex]

A Direct Method for Building Sparse Kernel Learning Algorithms

Wu, M., Schölkopf, B., BakIr, G.

Journal of Machine Learning Research, 7, pages: 603-624, April 2006 (article)

Abstract

Many Kernel Learning Algorithms(KLA), including Support Vector Machine (SVM), result in a Kernel Machine (KM), such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build Sparse Kernel Learning Algorithms (SKLA) by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting KM is explicitly controlled while at the same time the performance of the resulting KM can be kept as high as possible. A gradient based approach is provided to solve this modified optimization problem. Applying this method to the SVM results in a concrete algorithm for building Sparse Large Margin Classifiers (SLMC). Further analysis of the SLMC algorithm indicates that it essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated. Experimental results over several classification benchmarks demonstrate the effectiveness of our approach.

PDF PDF [BibTex]

Wu, M., Schölkopf, B., BakIr, G. A Direct Method for Building Sparse Kernel Learning Algorithms Journal of Machine Learning Research, 7, pages: 603-624, April 2006 (article)

PDF PDF [BibTex]

An Inventory of Sequence Polymorphisms For Arabidopsis

Clark, R., Ossowski, S., Schweikert, G., Rätsch, G., Shinn, P., Zeller, G., Warthmann, N., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D.

17th International Conference on Arabidopsis Research, April 2006 (talk)

Abstract

We have used high-density oligonucleotide arrays to characterize common sequence variation in 20 wild strains of Arabidopsis thaliana that were chosen for maximal genetic diversity. Both strands of each possible SNP of the 119 Mb reference genome were represented on the arrays, which were hybridized with whole genome, isothermally amplified DNA to minimize ascertainment biases. Using two complementary approaches, a model based algorithm, and a newly developed machine learning method, we identified over 550,000 SNPs with a false discovery rate of ~ 0.03 (average of 1 SNP for every 216 bp of the genome). A heuristic algorithm predicted in addition ~700 highly polymorphic or deleted regions per accession. Over 700 predicted polymorphisms with major functional effects (e.g., premature stop codons, or deletions of coding sequence) were validated by dideoxy sequencing. Using this data set, we provide the first systematic description of the types of genes that harbor major effect polymorphisms in natural populations at moderate allele frequencies. The data also provide an unprecedented resource for the study of genetic variation in an experimentally tractable, multicellular model organism.

[BibTex]

Clark, R., Ossowski, S., Schweikert, G., Rätsch, G., Shinn, P., Zeller, G., Warthmann, N., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D. An Inventory of Sequence Polymorphisms For Arabidopsis 17th International Conference on Arabidopsis Research, April 2006 (talk)

[BibTex]

Estimating Predictive Variances with Kernel Ridge Regression

Cawley, G., Talbot, N., Chapelle, O.

In MLCW 2005, pages: 56-77, (Editors: Quinonero-Candela, J. , I. Dagan, B. Magnini, F. D‘Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop, April 2006 (inproceedings)

Abstract

In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal sources of this uncertainty: the noise process contaminating the data and the uncertainty in estimating the model parameters based on a limited sample of training data. Both of them can be summarised in the predictive variance which can then be used to give confidence intervals. In this paper, we present various schemes for providing predictive variances for kernel ridge regression, especially in the case of a heteroscedastic regression, where the variance of the noise process contaminating the data is a smooth function of the explanatory variables. The use of leave-one-out cross-validation is shown to eliminate the bias inherent in estimates of the predictive variance. Results obtained on all three regression tasks comprising the predictive uncertainty challenge demonstrate the value of this approach.

PDF Web DOI [BibTex]

Cawley, G., Talbot, N., Chapelle, O. Estimating Predictive Variances with Kernel Ridge Regression In MLCW 2005, pages: 56-77, (Editors: Quinonero-Candela, J. , I. Dagan, B. Magnini, F. D‘Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop, April 2006 (inproceedings)

PDF Web DOI [BibTex]

Machine Learning and Applications in Biology

Shin, H.

6th Course in Bioinformatics for Molecular Biologist, March 2006 (talk)

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 biologists and machine learners might be made smoother.

PDF [BibTex]

Shin, H. Machine Learning and Applications in Biology 6th Course in Bioinformatics for Molecular Biologist, March 2006 (talk)

PDF [BibTex]

The Pedestal Effect is Caused by Off-Frequency Looking, not Nonlinear Transduction or Contrast Gain-Control

Wichmann, F., Henning, G.

9, pages: 174, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract

The pedestal or dipper effect is the large improvement in the detectability of a sinusoidal grating observed when the signal is added to a pedestal or masking grating having the signal‘s spatial frequency, orientation, and phase. The effect is largest with pedestal contrasts just above the threshold in the absence of a pedestal. We measured the pedestal effect in both broadband and notched masking noise---noise from which a 1.5-octave band centered on the signal and pedestal frequency had been removed. The pedestal effect persists in broadband noise, but almost disappears with notched noise. The spatial-frequency components of the notched noise that lie above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies that are different from that of the signal and pedestal. Thus the pedestal or dipper effect is not a characteristic of individual spatial-frequency tuned channels.

Web [BibTex]

Wichmann, F., Henning, G. The Pedestal Effect is Caused by Off-Frequency Looking, not Nonlinear Transduction or Contrast Gain-Control 9, pages: 174, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Web [BibTex]

Efficient tests for the deconvolution hypothesis

Langovoy, M.

Workshop on Statistical Inverse Problems, March 2006 (poster)

Web [BibTex]

Langovoy, M. Efficient tests for the deconvolution hypothesis Workshop on Statistical Inverse Problems, March 2006 (poster)

Web [BibTex]

Kernel extrapolation

Vishwanathan, SVN., Borgwardt, KM., Guttman, O., Smola, AJ.

Neurocomputing, 69(7-9):721-729, March 2006 (article)

Abstract

We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled method to combine many of these kernels and then extrapolate them. Central to our method is a theorem for matrix approximation, and an extension of the representer theorem to handle multiple joint regularization constraints. Experiments in protein classification demonstrate the feasibility of our approach.

Web DOI [BibTex]

Vishwanathan, SVN., Borgwardt, KM., Guttman, O., Smola, AJ. Kernel extrapolation Neurocomputing, 69(7-9):721-729, March 2006 (article)

Web DOI [BibTex]

ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments

Zhang, K., Chan, L.

In Independent Component Analysis and Blind Signal Separation, pages: 311-318, (Editors: J P Rosca and D Erdogmus and J C Príncipe and S Haykin), Springer, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA), March 2006, Series: Lecture Notes in Computer Science, Vol. 3889 (inproceedings)

Web [BibTex]

Zhang, K., Chan, L. ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments In Independent Component Analysis and Blind Signal Separation, pages: 311-318, (Editors: J P Rosca and D Erdogmus and J C Príncipe and S Haykin), Springer, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA), March 2006, Series: Lecture Notes in Computer Science, Vol. 3889 (inproceedings)

Web [BibTex]

Statistical Properties of Kernel Principal Component Analysis

Blanchard, G., Bousquet, O., Zwald, L.

Machine Learning, 66(2-3):259-294, March 2006 (article)

Abstract

We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local Rademacher averages, we provide data-dependent and tight bounds for their convergence towards eigenvalues of the corresponding kernel operator. We perform these computations in a functional analytic framework which allows to deal implicitly with reproducing kernel Hilbert spaces of infinite dimension. This can have applications to various kernel algorithms, such as Support Vector Machines (SVM). We focus on Kernel Principal Component Analysis (KPCA) and, using such techniques, we obtain sharp excess risk bounds for the reconstruction error. In these bounds, the dependence on the decay of the spectrum and on the closeness of successive eigenvalues is made explicit.

PDF PDF DOI [BibTex]

Blanchard, G., Bousquet, O., Zwald, L. Statistical Properties of Kernel Principal Component Analysis Machine Learning, 66(2-3):259-294, March 2006 (article)

PDF PDF DOI [BibTex]

Network-based de-noising improves prediction from microarray data

Kato, T., Murata, Y., Miura, K., Asai, K., Horton, P., Tsuda, K., Fujibuchi, W.

BMC Bioinformatics, 7(Suppl. 1):S4-S4, March 2006 (article)

Abstract

Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. We devised an extended version of the off-subspace noise-reduction (de-noising) method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson‘s correlation coefficient between the true and predicted respon se values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data. We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data.

PDF PDF DOI [BibTex]

Kato, T., Murata, Y., Miura, K., Asai, K., Horton, P., Tsuda, K., Fujibuchi, W. Network-based de-noising improves prediction from microarray data BMC Bioinformatics, 7(Suppl. 1):S4-S4, March 2006 (article)

PDF PDF DOI [BibTex]

Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning

Kuss, M.

Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

PDF [BibTex]

Kuss, M. Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

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Classification of Natural Scenes: Critical Features Revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

9, pages: 92, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract

Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Despite the seeming complexity of such decisions it has been hypothesized that a simple global image feature, the relative abundance of high spatial frequencies at certain orientations, could underly such fast image classification [1]. We successfully used linear discriminant analysis to classify a set of 11.000 images into animal and non-animal images based on their individual amplitude spectra only [2]. We proceeded to sort the images based on the performance of our classifier, retaining only the best and worst classified 400 images ("best animals", "best distractors" and "worst animals", "worst distractors"). We used a Go/No-go paradigm to evaluate human performance on this subset of our images. Both reaction time and proportion of correctly classified images showed a significant effect of classification difficulty. Images more easily classified by our algorithm were also classified faster and better by humans, as predicted by the Torralba & Oliva hypothesis. We then equated the amplitude spectra of the 400 images, which, by design, reduced algorithmic performance to chance whereas human performance was only slightly reduced [3]. Most importantly, the same images as before were still classified better and faster, suggesting that even in the original condition features other than specifics of the amplitude spectrum made particular images easy to classify, clearly at odds with the Torralba & Oliva hypothesis.

Web [BibTex]

Drewes, J., Wichmann, F., Gegenfurtner, K. Classification of Natural Scenes: Critical Features Revisited 9, pages: 92, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Web [BibTex]

Machine Learning Methods For Estimating Operator Equations

Steinke, F., Schölkopf, B.

In Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), pages: 6, (Editors: B Ninness and H Hjalmarsson), Elsevier, Oxford, United Kingdom, 14th IFAC Symposium on System Identification (SYSID), March 2006 (inproceedings)

Abstract

We consider the problem of fitting a linear operator induced equation to point sampled data. In order to do so we systematically exploit the duality between minimizing a regularization functional derived from an operator and kernel regression methods. Standard machine learning model selection algorithms can then be interpreted as a search of the equation best fitting given data points. For many kernels this operator induced equation is a linear differential equation. Thus, we link a continuous-time system identification task with common machine learning methods. The presented link opens up a wide variety of methods to be applied to this system identification problem. In a series of experiments we demonstrate an example algorithm working on non-uniformly spaced data, giving special focus to the problem of identifying one system from multiple data recordings.

PDF Web [BibTex]

Steinke, F., Schölkopf, B. Machine Learning Methods For Estimating Operator Equations In Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), pages: 6, (Editors: B Ninness and H Hjalmarsson), Elsevier, Oxford, United Kingdom, 14th IFAC Symposium on System Identification (SYSID), March 2006 (inproceedings)

PDF Web [BibTex]

Data mining problems and solutions for response modeling in CRM

Cho, S., Shin, H., Yu, E., Ha, K., MacLachlan, D.

Entrue Journal of Information Technology, 5(1):55-64, March 2006 (article)

Abstract

We present three data mining problems that are often encountered in building a response model. They are robust modeling, variable selection and data selection. Respective algorithmic solutions are given. They are bagging based ensemble, genetic algorithm based wrapper approach and nearest neighbor-based data selection in that order. A real world data set from Direct Marketing Educational Foundation, or DMEF4, is used to show their effectiveness. Proposed methods were found to solve the problems in a practical way.

PDF [BibTex]

Cho, S., Shin, H., Yu, E., Ha, K., MacLachlan, D. Data mining problems and solutions for response modeling in CRM Entrue Journal of Information Technology, 5(1):55-64, March 2006 (article)

PDF [BibTex]

Factorial Coding of Natural Images: How Effective are Linear Models in Removing Higher-Order Dependencies?

Bethge, M.

9, pages: 90, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract

The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform coding framework, where it can be characterized by the constraint of a triangular filter matrix. A randomly sampled whitening basis and the Haar wavelet are included into the comparison as well. The comparison of all these methods is carried out for different patch sizes, ranging from 2x2 to 16x16 pixels. In spite of large differences in the shape of the basis functions, we find only small differences in the multi-information between all decorrelation transforms (5% or less) for all patch sizes. Among the second-order methods, PCA is optimal for small patch sizes and predictive coding performs best for large patch sizes. The extra gain achieved with ICA is always less than 2%. In conclusion, the `edge filters‘ found with ICA lead only to a surprisingly small improvement in terms of its actual objective.

Web [BibTex]

Bethge, M. Factorial Coding of Natural Images: How Effective are Linear Models in Removing Higher-Order Dependencies? 9, pages: 90, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Web [BibTex]

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