On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution

In probabilistic PCA, the fully Bayesian estimation is computationally intractable. To cope with this problem, two types of approximation schemes were introduced: the partially Bayesian PCA (PB-PCA) where only the latent variables are integrated out, and the variational Bayesian PCA (VB-PCA) where the loading vectors are also integrated out. The VB-PCA was proposed as an improved variant of PB-PCA for enabling automatic dimensionality selection (ADS). In this paper, we investigate whether VB-PCA is really the best choice from the viewpoints of computational efficiency and ADS.We first show that ADS is not the unique feature of VB-PCA—PBPCA is also actually equipped with ADS.We further show that PB-PCA is more advantageous in computational efficiency than VB-PCA because the global solution of PB-PCA can be computed analytically. However, we also show the negative fact that PB-PCA results in a trivial solution in the empirical Bayesian framework. We next consider a simplified variant of VB-PCA, where the latent variables and loading vectors are assumed to be mutually independent (while the ordinary VB-PCA only requires matrix-wise independence). We show that this simplified VBPCA is the most advantageous in practice because its empirical Bayes solution experimentally works as well as the original VB-PCA, and its global optimal solution can be computed efficiently in a closed form. Appearing in Proceedings of the 28 th International Conference on Machine Learning, Bellevue, WA, USA, 2011. Copyright 2011 by the author(s)/owner(s).