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The dimensionality of sample is often larger than the number of training samples for high-dimensional pattern recognition such as face recognition. Here, Linear Discriminant Analysis (LDA) cannot be performed directly because of the singularity of the within-class scatter matrix. This is so-called "Small Sample Size" (SSS) problem. PCA plus LDA (FDA) and Direct LDA (DLDA) are two popular methods to solve the SSS problem of LDA. In this paper, the authors point out the relationship of these two methods and discuss the deficiency of DLDA. Then a Normalized Direct Linear Discriminant Analysis (NDLDA) method which overcomes DLDA's deficiency is proposed.
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