Multilayer perceptron based dimensionality reduction

Abstract

Dimensionality reduction is the process of mapping high dimensional patterns to a lower dimensional manifold and is typically used for visualization or as a preprocessing step in classification applications. From a classification viewpoint, the rate of increase of Bayes error serves as an ideal choice to measure the loss of information relevant to classification. Motivated by that, we present a multilayer perceptron which produces as output the lower dimensional representation. The multilayer perceptron is trained so as to minimize the classification error in the subspace. It thus differs from autoassociative like multilayer perceptrons which have been proposed and used for dimensionality reduction. We examine the performance of the proposed method of dimensionality reduction and the effect that varying the parameters have on the algorithm.