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An efficient discriminant-based solution for small sample size problem
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DOI：10.1016/j.patcog.2008.08.036.png)
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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
Keyword:
Feature extraction
Principal component analysis
Classification
Linear discriminant analysis
Bayes error
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7.6
论文数:
1.3W
被引数:
4.5W
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