An efficient discriminant-based solution for small sample size problem

  • Authors:

  • Koel Das;Zoran Nenadic

  • Affiliations:

  • Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA;Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA and Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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Abstract

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.