Near-optimal algorithm for dimension reduction

Abstract

Dimension reduction is a process of transforming the multidimensional observations into low-dimensional space. In pattern recognition this process should not cause loss of classification accuracy. This goal is best accomplished using Bayes error as a criterion for dimension reduction. Since the criterion is not usable for practical purposes, the authors suggest the use of the k-nearest neighbor estimate of the Bayes error instead. They experimentally demonstrate the superior performance of the linear dimension reduction algorithm based on this criterion, as compared to the traditional techniques.<>