PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Transformations in Variational Bayesian Factor Analysis to Speed Up Learning
Jaakko Luttinen and Alexander Ilin
Neurocomputing Volume 73, Number 7-9, pp. 1093-1102, 2010.


We propose simple transformation of the hidden states in variational Bayesian factor analysis models to speed up the learning procedure. The speed-up is achieved by using proper parameterization of the posterior approximation which allows joint optimization of its individual factors, thus the transformation is theoretically justified. We derive the transformation formulae for variational Bayesian factor analysis and show experimentally that it can significantly improve the rate of convergence. The proposed transformation basically performs centering and whitening of the hidden factors taking into account the posterior uncertainties. Similar transformations can be applied to other variational Bayesian factor analysis models as well.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7867
Deposited By:Alexander Ilin
Deposited On:17 March 2011