PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discriminative components of data
Jaakko Peltonen and Samuel Kaski
IEEE Transactions on Neural Networks Volume 16, Number 1, pp. 68-83, 2005. ISSN 1045-9227

Abstract

Preprint abstract: A simple probabilistic model is introduced to generalize classical linear discriminant analysis in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to (i) maximizing mutual information with the classes, and (ii) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost. ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

EPrint Type:Article
Additional Information:http://www.cis.hut.fi/sami/miabstracts/tnn04.html
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:287
Deposited By:Jaakko Peltonen
Deposited On:23 November 2004