Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.

ABSTRACT: We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance matrices, also induce conditional independence structures via zeros in the implied precision matrices. We describe the models and their use for robust estimation of sparse latent factor structure and data/signal reconstruction. We develop computational algorithms for model exploration and posterior mode search, addressing the hard combinatorial optimization involved in the search over a huge space of potential sparse configurations. A mean-field variational technique coupled with annealing is developed to

SUBMITTER: Yoshida R 

PROVIDER: S-EPMC2947451 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature