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Linear discriminant analysis (LDA) is a popular approach for dimensionality reduction for pattern classification; however, its performance is often degraded when samples are too few, particularly when the dimensionality of the input feature space is excessively high. The classic solution to the small-sample-size problem is to implement LDA in a principal component (PC) subspace, i.e., a strategy known...
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