  Group factor analysis (GFA) methods have been widely used to infer the common
structure and the group-specific signals from multiple related datasets in
various fields including systems biology and neuroimaging. To date, most
available GFA models require Gibbs sampling or slice sampling to perform
inference, which prevents the practical application of GFA to large-scale data.
In this paper we present an efficient collapsed variational inference (CVI)
algorithm for the nonparametric Bayesian group factor analysis (NGFA) model
built upon an hierarchical beta Bernoulli process. Our CVI algorithm proceeds
by marginalizing out the group-specific beta process parameters, and then
approximating the true posterior in the collapsed space using mean field
methods. Experimental results on both synthetic and real-world data demonstrate
the effectiveness of our CVI algorithm for the NGFA compared with
state-of-the-art GFA methods.
