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Indefinite Kernel Discriminant Analysis

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • Bernard Haasdonk

    (Institute of Applied Analysis and Numerical Simulation, University of Stuttgart)

  • Elżbieta Pȩkalska

    (University of Manchester, School of Computer Science)

Abstract

Kernel methods for data analysis are frequently considered to be restricted to positive definite kernels. In practice, however, indefinite kernels arise e.g. from problem-specific kernel construction or optimized similarity measures.We, therefore, present formal extensions of some kernel discriminant analysis methods which can be used with indefinite kernels. In particular these are the multi-class kernel Fisher discriminant and the kernel Mahalanobis distance. The approaches are empirically evaluated in classification scenarios on indefinite multi-class datasets.

Suggested Citation

  • Bernard Haasdonk & Elżbieta Pȩkalska, 2010. "Indefinite Kernel Discriminant Analysis," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 221-230, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_20
    DOI: 10.1007/978-3-7908-2604-3_20
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