Pattern classification using projection pursuit

Abstract

This article discusses the adaptation of recently developed regression techniques to classifier design. Apart from finite sample effects, projection pursuit (PP) regression may be used to model a desired response (class) as a sum of ridge functions according to a minimum expected squared error criterion. This approach can be shown to furnish an optimal discriminant function which can satisfy the Neyman-Pearson criterion over all possible thresholds. Basis function expansions are used instead of smoothed histograms to reduce computation. Since good approximation of a discriminant by a linear combination of moderate number of ridge functions may not be easy, we introduce an improved method utilizing a nonlinear weighting function.