PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast Semi-supervised Discriminative Component Analysis
Jaakko Peltonen, Jacob Goldberger and Samuel Kaski
In: 2007 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2007), 27-29 Aug 2007, Thessaloniki, Greece.

Abstract

We introduce a method that learns a class-discriminative subspace or discriminative components of data. Such a subspace is useful for visualization, dimensionality reduction, feature extraction, and for learning a regularized distance metric. We learn the subspace by optimizing a probabilistic semiparametric model, a mixture of Gaussians, of classes in the subspace. The semiparametric modeling leads to fast computation (O(N) for N samples) in each iteration of optimization, in contrast to recent nonparametric methods that take O(N^2) time, but with equal accuracy. Moreover, we learn the subspace in a semi-supervised manner from three kinds of data: labeled and unlabeled samples, and unlabeled samples with pairwise constraints, with a unified objective. ©2007 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:Conference or Workshop Item (Paper)
Additional Information:http://www.cis.hut.fi/projects/mi/abstracts/mlsp07.html
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:3027
Deposited By:Jaakko Peltonen
Deposited On:30 August 2007