We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
Dimensional reduction is a simplification technique that eliminates one or more dimensions from a boundary value problem. It results in significant computational savings with mini...
Abstract. Mutual Information (MI) is a long studied measure of information content, and many attempts to apply it to feature extraction and stochastic coding have been made. Howeve...
In this paper, our main contribution is a framework for the automatic configuration of any spectral dimensionality reduction methods. This is achieved, first, by introducing the m...
Michal Lewandowski, Dimitrios Makris, Jean-Christo...
In usual ICA methods, sources are typically estimated by maximizing a measure of their statistical independence. This paper explains how to perform non-linear ICA by preprocessing ...