A Kernel Optimization Method Based on the Localized Kernel Fisher Criterion

  • Bo Chen
  • Hongwei Liu
  • Zheng Bao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


It is wildly recognized that whether the selected kernel matches the data controls the performance of kernel-based methods. Ideally it is expected that the data is linearly separable in the kernel induced feature space, therefore, Fisher linear discriminant criterion can be used as a kernel optimization rule. However, the data may not be linearly separable even after kernel transformation in many applications, a nonlinear classifier is preferred in this case, and obviously the Fisher criterion is not the best choice as a kernel optimization rule. Motivated by this issue, in this paper we present a novel kernel optimization method by maximizing the local class linear separability in kernel space to increase the local margins between embedded classes via localized kernel Fisher criterion, by which the classification performance of nonlinear classifier in the kernel induced feature space can be improved. Extensive experiments are carried out to evaluate the efficiency of the proposed method.


Feature Space Classification Performance Gaussian Mixture Model Fisher Criterion Kernel Optimization 
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  1. 1.
    Xiong, H.L., Swamy, M.N.S., Ahmad, M.O.: Optimizing The Kernel In The Empirical Feature Space. IEEE Trans. Neural Networks 16(2), 460–474 (2005)CrossRefGoogle Scholar
  2. 2.
    Amari, S., Wu, S.: Improving Support Vector Machine Classifiers By Modifying Kernel Functions. Neural Networks 12(6), 783–789 (1999)CrossRefGoogle Scholar
  3. 3.
    Ruiz, A., Lopez-de Teruel, P.E.: Nonlinear Kernel-based Statistical Pattern Analysis. IEEE Trans. Neural Networks 12(1), 16–32 (2001)CrossRefGoogle Scholar
  4. 4.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge Univ. Press, Cambridge (1996)MATHGoogle Scholar
  5. 5.
    Blake, C., Keogh, E., Merz, C.J.: UCI Repository Of Machine Learning Databases. Dept. Inform. Comput. Sci. Univ. California, Irvine (1998) [Online] Available, Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo Chen
    • 1
  • Hongwei Liu
    • 1
  • Zheng Bao
    • 1
  1. 1.National Lab of Radar Signal ProcessingXidian UniversityXi’anP.R. China

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