关键词: Fisher鉴别分析, 核函数, 模式识别, 特征空间

Abstract: Kernel Fisher discriminant method performs well in classification; however, the fact that the classification efficiency associated with KFD descends when the number of training samples increases makes against its application. In this paper an optimized scheme on kernel Fisher discriminant method is proposed, which is with superiority in classification efficiency. The corresponding algorithms are developed for two-class problems and multi-class problems, respectively. In the scheme, only a part of training patterns, called “significant nodes”, are necessary to be adopted in classifying one test pattern. A recursive algorithm for selecting “significant nodes” is also presented in detail. The experiment on benchmarks and face image databases show that the novel method is effective and much efficient in classifying.

Key words: Fisher discriminant analysis, Kernel function, Pattern recognition, Feature space