Optimally regularised kernel Fisher discriminant analysis

Saadi, K., Talbot, N. L. C. and Cawley, G. C. (2004) Optimally regularised kernel Fisher discriminant analysis. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004), 2004-08-23 - 2004-08-26.

Full text not available from this repository. (Request a copy)

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

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 21 Jun 2011 17:17
Last Modified: 22 Apr 2020 09:13
URI: https://ueaeprints.uea.ac.uk/id/eprint/22811
DOI: 10.1109/ICPR.2004.1334245

Actions (login required)

View Item View Item