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Cawley, Gavin C. 
ORCID: https://orcid.org/0000-0002-4118-9095 and Talbot, Nicola L. C.
(2003)
Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers.
Pattern Recognition, 36 (11).
pp. 2585-2592.
ISSN 0031-3203
Abstract
Mika et al. (in: Neural Network for Signal Processing, Vol. IX, IEEE Press, New York, 1999; pp. 41–48) apply the “kernel trick” to obtain a non-linear variant of Fisher's linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark data sets. We show that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only $\mathcal{O}(\ell^3)$ operations rather than the $\mathcal{O}(\ell^4)$ of a naïve implementation, where l is the number of training patterns. Leave-one-out cross-validation then becomes an attractive means of model selection in large-scale applications of kernel Fisher discriminant analysis, being significantly faster than conventional k-fold cross-validation procedures commonly used.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 13 Jun 2011 11:26 |
| Last Modified: | 22 Apr 2023 01:55 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22157 |
| DOI: | 10.1016/S0031-3203(03)00136-5 |
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