An Adaptive Nonparametric Discriminant Analysis Method
Zhao Yu-min · Microcomputer Information · 2009
Linear Discriminant Analysis(LDA)is frequently used for dimension reduction and has been successfully utilized in many applications,especially face recognition.In classical LDA,however,the definition of the between-class scatter matrix can cause large overlaps between neighboring classes,because LDA assumes that all classes obey a Gaussian distribution with the same covariance. We therefore,propose an adaptive nonparametric discriminant analysis(ANDA)algorithm that maximizes the distance between neigh- boring samples belonging to different classes,thus improving the discriminating power of the samples near the classification borders. To evaluate its performance thoroughly,we have compared our ANDA algorithm with traditional PCA+LDA,Orthogonal LDA(OLDA) and nonparametric discriminant analysis(NDA)on the FERET and ORL face databases.Experimental results show that the proposed algorithm outperforms the others.