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A Sparse Support Vector Machine Classifier with Nonparametric Discriminants

  • Naimul Mefraz Khan
  • Riadh Ksantini
  • Imran Shafiq Ahmad
  • Ling Guan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

This paper introduces a novel Sparse Support Vector Machine model with Kernel Nonparametric Discriminants (SSVMKND) which combines data distribution information from two classifiers, namely, the Kernel Support Vector Machine (KSVM) and the Kernel Nonparametric Discriminant (KND). It is a convex quadratic optimization problem with one global solution, so it can be estimated efficiently with the help of numerical methods. It can also be reduced to the classical KSVM model, and existing SVM programs can be used for easy implementation. We show that our method provides a sparse solution through the Bayesian interpretation. This sparsity can be used by existing sparse classification algorithms to obtain better computational efficiency. The experimental results on real-world datasets and face recognition applications show that the proposed SSVMKND model improves the classification accuracy over other classifiers and also provides sparser solution.

Keywords

Nonparametric Discriminant Analysis Support Vector Machines Sparsity Gaussian Prior kernel face recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Naimul Mefraz Khan
    • 1
  • Riadh Ksantini
    • 2
  • Imran Shafiq Ahmad
    • 2
  • Ling Guan
    • 1
  1. 1.Department of Electrical and Computer EngineeringRyerson UniversityCanada
  2. 2.School of Computer ScienceUniversity of WindsorCanada

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