PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dimensionality reduction for clustered data sets
Guido Sanguinetti
IEEE Trans. Pattern Analysis Machine Intelligence Volume 30, Number 3, 2008.

Abstract

We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:3515
Deposited By:Guido Sanguinetti
Deposited On:11 February 2008