Optimally regularised kernel Fisher discriminant analysis

Research output: Contribution to conferencePaper

5 Citations (Scopus)

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.
Original languageEnglish
Pages427-430
Number of pages4
DOIs
Publication statusPublished - Aug 2004
EventProceedings of the 17th International Conference on Pattern Recognition (ICPR-2004) -
Duration: 23 Aug 200426 Aug 2004

Conference

ConferenceProceedings of the 17th International Conference on Pattern Recognition (ICPR-2004)
Period23/08/0426/08/04

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