An efficient discriminant-based solution for small sample size problem

Koel Das

doi:10.1016/j.patcog.2008.08.036

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An efficient discriminant-based solution for small sample size problem

2009-05-01

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Koel Das

Zoran Nenadić *

DOI：10.1016/j.patcog.2008.08.036

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Abstract

En 中文

Classification of high-dimensional statistical data is usually not amenable to standard pattern recognition techniques because of an underlying small sample size problem. To address the problem of high-dimensional data classification in the face of a limited number of samples, a novel principal component analysis (PCA) based feature extraction/classification scheme is proposed. The proposed method yields a piecewise linear feature subspace and is particularly well-suited to difficult recognition problems where achievable classification rates are intrinsically low. Such problems are often encountered in cases where classes are highly overlapped, or in cases where a prominent Curvature in data renders a projection onto a single linear subspace inadequate. The proposed feature extraction/classification method uses class-dependent PCA in Conjunction With linear discriminant feature extraction and performs well on a variety of real-world datasets, ranging from digit recognition to classification of high-dimensional bioinformatics and brain imaging data. (C) 2008 Elsevier Ltd. All rights reserved

Keywords:

Feature extraction

Principal component analysis

Classification

Linear discriminant analysis

Bayes error

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