Abstract
Linear discriminant analysis (LDA) often encounters small sample size (SSS) problem for high-dimensional data. Null space linear discriminant analysis (NLDA) and linear discriminant analysis based on generalized singular value decomposition (LDA/GSVD) are two popular methods that can solve SSS problem of LDA. In this paper, we present the relation between NLDA and LDA/GSVD under a condition and at the same time propose a modified NLDA (MNLDA) algorithm which has the same discriminating power as LDA/GSVD and is more efficient. In addition, we compare the discriminating capability of NLDA and MNLDA and present our interpretation about this. Experimental results on ORL, FERET, Yale face databases, and the PolyU FKP database support our viewpoints.
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