A modification of kernel discriminant analysis for high-dimensional data - with application to face recognition

Abstract

Kernel discriminant analysis (KDA) is an effective statistical method for dimensionality reduction and feature extraction. However, traditional KDA methods suffer from the small sample size problem. Moreover, they endure the Fisher criterion that is nonoptimal with respect to classification rate. This paper presents a variant of KDA that deals with both of… (More)
DOI: 10.1016/j.sigpro.2009.09.025

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