Indefinite Kernel Discriminant Analysis

Bernard Haasdonk () and Elżbieta Pȩkalska ()
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Bernard Haasdonk: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart
Elżbieta Pȩkalska: University of Manchester, School of Computer Science

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 221-230 from Springer

Abstract: 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.

Keywords: kernel methods; indefinite kernels; Mahalanobis distance; Fisher Discriminant Analysis (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_20

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DOI: 10.1007/978-3-7908-2604-3_20

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