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.
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.