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Saadi, K., Talbot, N. L. C. and Cawley, G. C. 
ORCID: https://orcid.org/0000-0002-4118-9095
(2004)
Optimally regularised kernel Fisher discriminant analysis.
In: 17th International Conference on Pattern Recognition, 2004-08-23 - 2004-08-26.
Abstract
Mika et al. (1999) introduce a non-linear formulation of Fisher's linear discriminant, based the now familiar "kernel trick", demonstrating state-of-the-art performance on a wide range of real-world benchmark datasets. In this paper, we show that the usual regularisation parameter can be adjusted so as to minimise the leave-one-out cross-validation error with a computational complexity of only O(l2) operations, where l is the number of training patterns, rather than the O(l4) operations required for a naive implementation of the leave-one-out procedure. This procedure is then used to form a component of an efficient hierarchical model selection strategy where the regularisation parameter is optimised within the inner loop while the kernel parameters are optimised in the outer loop.
| 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: | 21 Jun 2011 17:17 |
| Last Modified: | 22 Apr 2023 02:46 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22811 |
| DOI: | 10.1109/ICPR.2004.1334245 |
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