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.