Linear Discriminant Analysis Based on L1-Norm Maximization
Abstract
Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the...
Paper Details
Title
Linear Discriminant Analysis Based on L1-Norm Maximization
Published Date
Aug 1, 2013
Volume
22
Issue
8
Pages
3018 - 3027
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