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Estimating vector fields using sparse basis field expansions 08

Estimating vector fields using sparse basis field expansions

引用

Proceedings of the 22nd International Conference on Neural Information Processing Systems

作者： Stefan Haufe Vadim V. Nikulin Andreas Ziehe Klaus-Robert Müller Guido Nolte TU Berlin Dept. of Computer Science Machine Learning Laboratory Berlin Germany and Fraunhofer Institute FIRST (IDA) Berlin Germany Charité University Medicine Dept. of Neurology Campus Benjamin Franklin Berlin Germany and Bernstein Center for Computational Neuroscience Berlin Germany TU Berlin Dept. of Computer Science Machine Learning Laboratory Berlin Germany and Fraunhofer Institute FIRST (IDA) Berlin Germany and Bernstein Center for Computational Neuroscience Berlin Germany Fraunhofer Institute FIRST (IDA) Berlin Germany

ISBN: (纸本)9781605609492

We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX). The notion of basis fields, which are an extension of scalar basis functions, arises naturally in our framework from a rotational invariance requirement. We consider a regression setting as well as inverse problems. All variants discussed lead to second-order cone programming formulations. While our framework is generally applicable to any type of vector field, we focus in this paper on applying it to solving the EEG/MEG inverse problem. It is shown that significantly more precise and neurophysiologically more plausible location and shape estimates of cerebral current sources from EEG/MEG measurements become possible with our method when comparing to the state-of-the-art.

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Semi-supervised learning with Weakly-Related Unlabeled Data: towards better text categorization 08

Semi-supervised learning with Weakly-Related Unlabeled Data:...

引用

Proceedings of the 22nd International Conference on Neural Information Processing Systems

作者： Liu Yang Rong Jin Rahul Sukthankar Machine Learning Dept. Carnegie Mellon University Pittsburgh PA Dept. of Computer Sci. and Eng. Michigan State University East Lansing MI Intel Research Pittsburgh and Carnegie Mellon Univ. Pittsburgh PA

ISBN: (纸本)9781605609492

The cluster assumption is exploited by most semi-supervised learning (SSL) methods. However, if the unlabeled data is merely weakly related to the target classes, it becomes questionable whether driving the decision boundary to the low density regions of the unlabeled data will help the classification. In such case, the cluster assumption may not be valid; and consequently how to leverage this type of unlabeled data to enhance the classification accuracy becomes a challenge. We introduce "Semi-supervised learning with Weakly-Related Unlabeled Data" (SSLW), an inductive method that builds upon the maximum-margin approach, towards a better usage of weakly-related unlabeled information. Although the SSLW could improve a wide range of classification tasks, in this paper, we focus on text categorization with a small training pool. The key assumption behind this work is that, even with different topics, the word usage patterns across different corpora tends to be consistent. To this end, SSLW estimates the optimal word-correlation matrix that is consistent with both the co-occurrence information derived from the weakly-related unlabeled documents and the labeled documents. For empirical evaluation, we present a direct comparison with a number of state-of-the-art methods for inductive semi-supervised learning and text categorization. We show that SSLW results in a significant improvement in categorization accuracy, equipped with a small training set and an unlabeled resource that is weakly related to the test domain.

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A sample selection algorithm in fuzzy decision tree induction and its theoretical analyses

A sample selection algorithm in fuzzy decision tree inductio...

引用

2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

作者： Wang, Xi-Zhao Yan, Jian-Hui Wang, Ran Dong, Chun-Ru IEEE Machine Learning Center Faculty of Mathematics and Computer Science Hebei University China Dept. of Computer Science Beijing Forestry University China

ISBN: (纸本)1424409918

The generalization capability of a classifier will probably be degenerated when the classifier is generated from a dataset containing redundancy. To remove the redundancy, sample selection methods which choose the most valuable and representative instances from the original date set, can be used to obtain a subset of the original dataset. It is expected that the classifier trained from the subset can achieve no lower generalization capability than the classifier trained from the original data set. This paper proposes a sample selection method based on maximum entropy of testing instances in the fuzzy decision tree induction, and also gives the related theoretical analyses. © 2007 IEEE.

关键词： Decision trees

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Mixtures of robust probabilistic principal component analyzers

Mixtures of robust probabilistic principal component analyze...

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15th European Symposium on Artificial Neural Networks, ESANN 2007

作者： Archambeau, Cédric Delannay, Nicolas Verleysen, Michel University College London Dept. of Computer Science Gower Street London WC1E 6BT United Kingdom Université catholique de Louvain Machine Learning Group 3 Place du Levant 1348 Louvain-la-Neuve Belgium

ISBN: (纸本)2930307099

Discovering low-dimensional (nonlinear) manifolds is an important problem in machine learning. In many applications, the data are in a high dimensional space. This can be problematic in practice due to the curse of dimensionality. Fortunately, the core of the data lies often on one or several low-dimensional manifolds. A way to handle these is to pre-process the data by nonlinear data projection techniques. Another approach is to combine local linear models. In particular, mixtures of probabilistic principal component analyzers are very attractive as each component is specifically designed to extract the local principal orientations in the data. However, an important issue is the model sensitivity to data lying off the manifold, possibly leading to mismatches between successive local models. The mixtures of robust probabilistic principal component analyzers introduced in this paper heal this problem as each component is able to cope with atypical data while identifying the local principal directions. Moreover, the standard mixture of Gaussians is a particular instance of this more general model.

关键词： Mixtures

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High-dimensional graphical model selection using 1- regularized logistic regression 19

引用

20th Annual Conference on Neural Information Processing Systems, NIPS 2006

作者： Wainwright, Martin J. Ravikumar, Pradeep Lafferty, John D. Department of Statistics Univ. of California Berkeley Berkeley CA 94720 United States Department of EECS Univ. of California Berkeley Berkeley CA 94720 United States Machine Learning Dept. Carnegie Mellon Univ. Pittsburgh PA 15213 United States Computer Science Dept. Carnegie Mellon Univ. Pittsburgh PA 15213 United States

ISBN: (纸本)9780262195683

We focus on the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on 1- regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an 1- constraint. Our framework applies to the high-dimensional setting, in which both the number of nodes p and maximum neighborhood sizes d are allowed to grow as a function of the number of observations n. Our main result is to establish sufficient conditions on the triple (n, p, d) for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. Under certain mutual incoherence conditions analogous to those imposed in previous work on linear regression, we prove that consistent neighborhood selection can be obtained as long as the number of observations n grows more quickly than 6d6 log d + 2d5 log p, thereby establishing that logarithmic growth in the number of samples n relative to graph size p is sufficient to achieve neighborhood consistency.

关键词： Graphic methods

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A probabilistic framework for content-based diagnosis of retinal disease

A probabilistic framework for content-based diagnosis of ret...

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29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07

作者： Tobin, Kenneth W. Abdelrahman, Mohamed Chaum, Edward Govindasamy, V. Priya Karnowski, Thomas P. IEEE Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN 37831-6075 United States Electrical and Computer Engineering Dept. Tennessee Tech. University Cookeville TN 38505-0001 United States Department of Retinal Diseases Hamilton Eye Institute University of Tennessee Memphis TN 38163 United States

ISBN: (纸本)1424407885

Diabetic retinopathy is the leading cause of blindness in the working age population in the industrialized world. computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is leading to the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states. © 2007 IEEE.

关键词： Diseases

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Invariant common spatial patterns: alleviating nonstationarities in Brain-computer Interfacing 07

Invariant common spatial patterns: alleviating nonstationari...

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Proceedings of the 20th International Conference on Neural Information Processing Systems

作者： Benjamin Blankertz Motoaki Kawanabe Ryota Tomioka Friederike U. Hohlefeld Vadim Nikulin Klaus-Robert Müller TU Berlin Dept. of Computer Science Machine Learning Laboratory Berlin Germany and Fraunhofer FIRST (IDA) Berlin Germany Fraunhofer FIRST (IDA) Berlin Germany Dept. Mathematical Informatics IST The University of Tokyo Japan Berlin School of Mind and Brain Berlin Germany Dept. of Neurology Campus Benjamin Franklin Charité University Medicine Berlin Germany

ISBN: (纸本)9781605603520

Brain-computer Interfaces can suffer from a large variance of the subject conditions within and across sessions. For example vigilance fluctuations in the individual, variable task involvement, workload etc. alter the characteristics of EEG signals and thus challenge a stable BCI operation. In the present work we aim to define features based on a variant of the common spatial patterns (CSP) algorithm that are constructed invariant with respect to such nonstationarities. We enforce invariance properties by adding terms to the denominator of a Rayleigh coefficient representation of CSP such as disturbance covariance matrices from fluctuations in visual processing. In this manner physiological prior knowledge can be used to shape the classification engine for BCI. As a proof of concept we present a BCI classifier that is robust to changes in the level of parietal α-activity. In other words, the EEG decoding still works when there are lapses in vigilance.

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A Probabilistic Framework for Content-Based Diagnosis of Retinal Disease

A Probabilistic Framework for Content-Based Diagnosis of Ret...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Kenneth W. Tobin Mohamed Abdelrahman Edward Chaum V. Priya Govindasamy Thomas P. Karnowski Image Science and Machine Vision Group at the Oak Ridge National Laboratory Oak Ridge Tennessee USA Electrical and Computer Engineering Dept. Tennessee Tech University Cookeville Tennessee USA Retinal Diseases at the University of Tennessee Health Science Center Hamilton Eye Institute Memphis TN USA

关键词： Retina Image retrieval Degenerative diseases Laboratories Image analysis Pathology Content based retrieval Cameras Blindness machine vision

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The support vector decomposition machine 06

The support vector decomposition machine

引用

23rd International Conference on machine learning, ICML 2006

作者： Pereira, Francisco Gordon, Geoffrey Computer Science Dept. Carnegie Mellon University Machine Learning Dept. Carnegie Mellon University Center for the Neural Basis of Cognition Carnegie Mellon University

ISBN: (纸本)1595933832

In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learning performance. In previous work, many researchers have treated the learning problem in two separate phases: first use an algorithm such as singular value decomposition to reduce the dimensionality of the data set, and then use a classification algorithm such as naïve Bayes or support vector machines to learn a classifier. We demonstrate that it is possible to combine the two goals of dimensionality reduction and classification into a single learning objective, and present a novel and efficient algorithm which optimizes this objective directly. We present experimental results in fMRI analysis which show that we can achieve better learning performance and lower-dimensional representations than two-phase approaches can.

关键词： Support vector machines

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Contextual search and name disambiguation in email using graphs

Contextual search and name disambiguation in email using gra...

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29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Minkov, Einat Cohen, William W. Ng, Andrew Y. Language Technologies Inst. Carnegie Mellon University Pittburgh PA 15213 United States Machine Learning Dept. Carnegie Mellon University Pittsburgh PA 15213 United States Computer Science Dept. Stanford University Stanford CA 94305 United States

ISBN: (纸本)1595933697

Similarity measures for text have historically been an important tool for solving information retrieval problems. In many interesting settings, however, documents are often closely connected to other documents, as well as other non-textual objects: for instance, email messages are connected to other messages via header information. In this paper we consider extended similarity metrics for documents and other objects embedded in graphs, facilitated via a lazy graph walk. We provide a detailed instantiation of this framework for email data, where content, social networks and a timeline are integrated in a structural graph. The suggested framework is evaluated for two email-related problems: disambiguating names in email documents, and threading. We show that reranking schemes based on the graph-walk similarity measures often outperform baseline methods, and that further improvements can be obtained by use of appropriate learning methods. Copyright 2006 ACM.

关键词： Electronic mail

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