A New Kernel Discriminant Analysis Algorithm and its Application to Face Recognition
一种新的核线性鉴别分析算法及其在人脸识别上的应用

Kernel Fisher discriminative analysis(KFD)algorithm based on kernel trick has been one of the effective nonlinear feature extraction methods.But all previous nonlinear feature extraction methods based on KFD algorithm which procedures are based on solving binary classification problem.How to extract effective discriminative information from overlapping(outlier)samples is still open.In this paper,a new KFD algorithm named fuzzy kernel discriminative analysis(FKFD)is proposed.In the proposed algorithm,fuzzy K-nearest neighbor(FKNN)algorithm is incorporated into the process of KFD algorithm and the corresponding fuzzy membership degrees are gained.Therefore,distribution information of samples is embedded in the proposed algorithm through fuzzy membership degrees.Experimental results on ORL face database demonstrate the effectiveness of the proposed algorithm.