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Abstract: In this paper we consider a novel Bayesian interpretation of Fisher's discriminant analysis. We relate Rayleigh's coefficient to a noise model that minimises a cost based on the most probable class centres and that abandons the `regression to the labels' assumption used by other algorithms. Optimisation of the noise model yields a direction of discrimination equivalent to Fisher's discriminant, and with the incorporation of a prior we can apply Bayes' rule to infer the posterior... (Update)
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BibTeX entry: (Update)
@misc{ analysis-journal,
author = "Non-Linear Discriminant Analysis",
title = "Journal of Machine Learning Research 7 (2006) 455--491 Submitted 12/04;
Revised 10/05; Published 2/06 Optimising Kernel Parameters and Regularisation
Coefficients for",
url = "citeseer.ist.psu.edu/759848.html" }
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