PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
Tonatiuh Pena-Centeno and Neil Lawrence
(2004) Technical Report. University of Sheffield, Sheffield, U.K..

Abstract

In this paper we consider a Bayesian interpretation of Fisher's discriminant. By relating Rayleigh's coefficient to a likelihood function and through the choice of a suitable prior we use Bayes' rule to infer a posterior distribution over projections. Through the use of a Gaussian process prior we show the equivalence of our model to a regularised kernel Fisher's discriminant. A key advantage of our approach is the facility to determine kernel parameters and the regularisation coefficient through optimisation of the marginalised likelihood of the data.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:915
Deposited By:Neil Lawrence
Deposited On:06 January 2005