Efficient cross-validation of kernel Fisher discriminant classifiers

Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095 and Talbot, Nicola L. C. (2003) Efficient cross-validation of kernel Fisher discriminant classifiers. In: European Symposium on Artificial Neural Networks, 2003-04-23 - 2003-04-25.

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Mika et al. [1] introduce a non-linear formulation of the Fisher discriminant based the well-known "kernel trick", later shown to be equivalent to the Least-Squares Support Vector Machine [2, 3]. In this paper, we show that the cross-validation error can be computed very efficiently for this class of kernel machine, specifically that leave-one-out cross-validation can be performed with a computational complexity of only O(l3) operations (the same as that of the basic training algorithm), rather than the O(l4) of a direct implementation. This makes leave-one-out cross-validation a practical proposition for model selection in much larger scale applications of KFD classifiers.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences

UEA Research Groups: Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User: Vishal Gautam
Date Deposited: 22 Jul 2011 13:11
Last Modified: 20 Jun 2023 14:34
URI: https://ueaeprints.uea.ac.uk/id/eprint/22156

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