Complexity Reduction of Kernel Discriminant Analysis

Abstract

As an extension of the linear discriminant analysis (LDA), the kernel discriminant analysis (KDA) generally results in good pattern recognition performance for both small sample size (SSS) and non-SSS problems. Yet, the original scheme based on the eigen-decomposition technique suffers from a complexity burden. In this paper, by transforming the problem of finding the feature extractor (FE) of the KDA into a linear equation problem, reduction of the complexity is accomplished via a novel scheme for the FE of the KDA.