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A direct LDA algorithm for high-dimensional data -- with application to face recognition. (English) Zbl 0993.68091
Summary: We proposed a direct LDA algorithm for high-dimensional data classification, with application to face recognition in particular. Since the number of samples is typically smaller than the dimensionality of the samples, both $S_b$ and $S_w$ are singular. By modifying the simultaneous diagonalization procedure, we are able to discard the null space of $S_b$ -- which carries no discriminative information -- and to keep the null space of $S_w$, which is very important for classification. In addition, computational techniques are introduced to handle large scatter matrices efficiently. The result is a unified LDA algorithm that gives an exact solution to Fisher’s criterion whether or not $S_w$ is singular.

68T10Pattern recognition, speech recognition
68W05Nonnumerical algorithms
Full Text: DOI
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