Fisher Discriminant Analysis With L1-Norm

Abstract

Fisher linear discriminant analysis (LDA) is a classical subspace learning technique of extracting discriminative features for pattern recognition problems. The formulation of the Fisher criterion is based on the L2-norm, which makes LDA prone to being affected by the presence of outliers. In this paper, we propose a new method, termed LDA-L1, by maximizing… (More)
DOI: 10.1109/TCYB.2013.2273355

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