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Learning the optimal kernel for Fisher discriminant analysis via second order cone programming

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  • Khemchandani, Reshma
  • Jayadeva
  • Chandra, Suresh

Abstract

Kernel Fisher discriminant analysis (KFDA) is a popular classification technique which requires the user to predefine an appropriate kernel. Since the performance of KFDA depends on the choice of the kernel, the problem of kernel selection becomes very important. In this paper we treat the kernel selection problem as an optimization problem over the convex set of finitely many basic kernels, and formulate it as a second order cone programming (SOCP) problem. This formulation seems to be promising because the resulting SOCP can be efficiently solved by employing interior point methods. The efficacy of the optimal kernel, selected from a given convex set of basic kernels, is demonstrated on UCI machine learning benchmark datasets.

Suggested Citation

  • Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2010. "Learning the optimal kernel for Fisher discriminant analysis via second order cone programming," European Journal of Operational Research, Elsevier, vol. 203(3), pages 692-697, June.
    • Handle: RePEc:eee:ejores:v:203:y:2010:i:3:p:692-697
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    References listed on IDEAS

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    1. Yajima, Yasutoshi, 2005. "Linear programming approaches for multicategory support vector machines," European Journal of Operational Research, Elsevier, vol. 162(2), pages 514-531, April.
    2. Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2009. "Knowledge based proximal support vector machines," European Journal of Operational Research, Elsevier, vol. 195(3), pages 914-923, June.
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