Complexity reduction of kernel discriminant analysis
Yuxi Hou, Hwang-Ki Min, Seungwon Lee, Seokho Yoon, Seong Ro Lee, Iickho Song · 2012
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.