2010 | OriginalPaper | Buchkapitel
Indefinite Kernel Discriminant Analysis
verfasst von : Bernard Haasdonk, Elżbieta Pȩkalska
Erschienen in: Proceedings of COMPSTAT'2010
Verlag: Physica-Verlag HD
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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.