Kernel Optimization Using a Generalized Eigenvalue Approach

  • Jayadeva
  • Sameena Shah
  • Suresh Chandra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

There is no single generic kernel that suits all estimation tasks. Kernels that are learnt from the data are known to yield better classification. The coefficients of the optimal kernel that maximizes the class separability in the empirical feature space had been previously obtained by a gradient-based procedure. In this paper, we show how these coefficients can be learnt from the data by simply solving a generalized eigenvalue problem. Our approach yields a significant reduction in classification errors on selected UCI benchmarks.

Keywords

Data dependent kernel Fisher’s coefficient generalized eigenvalue kernel optimization Rayleigh quotient 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jayadeva
    • 1
  • Sameena Shah
    • 1
  • Suresh Chandra
    • 1
  1. 1.Indian Institute of Technology DelhiNew DelhiIndia

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