Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers

Cawley, G. C. and Talbot, N. L. C. (2003) Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognition, 36 (11). pp. 2585-2592. ISSN 0031-3203

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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
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 13 Jun 2011 11:26
Last Modified: 24 Jul 2019 16:51
URI: https://ueaeprints.uea.ac.uk/id/eprint/22157
DOI: 10.1016/S0031-3203(03)00136-5

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