PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Transformations for Variational Factor Analysis to Speed up Learning
Jaakko Luttinen, Alexander Ilin and Tapani Raiko
In: ESANN 2009, 22-24 April 2009, Bruges, Belgium.

Abstract

We propose simple transformation of the hidden states in variational Bayesian (VB) factor analysis models to speed up the learning procedure. The transformation basically performs centering and whitening of the hidden states taking into account the posterior uncertainties. The transformation is given a theoretical justification from optimisation of the VB cost function. We derive the transformation formulae for variational Bayesian principal component analysis and show experimentally that it can significantly improve the rate of convergence. Similar transformations can be applied to other variational Bayesian factor analysis models as well.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6403
Deposited By:Tapani Raiko
Deposited On:08 March 2010