Automatic model selection for probabilistic PCA

Authors:
Ezequiel López-Rubio;Juan Miguel Ortiz-de-Lazcano-Lobato;Domingo López-Rodríguez;María Del Carmen Vargas-González
Affiliations:
School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain;School of Computer Engineering, University of Málaga, Málaga, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 3
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Abstract

The Mixture of Probabilistic Principal Components Analyzers (MPPCA) is a multivariate analysis technique which defines a Gaussian probabilistic model at each unit. The number of units and principal directions in each unit is not learned in the original approach. Variational Bayesian approaches have been proposed for this purpose, which rely on assumptions on the input distribution and/or approximations of certain statistics. Here we present a different way to solve this problem, where cross-validation is used to guide the search for an optimal model selection. This allows to learn the model architecture without the need of any assumptions other than those of the basic PPCA framework. Experimental results are presented, which show the probability density estimation capabilities of the proposal with high dimensional data.