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

@article{Yoshida2010BayesianLI,
  title={Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing},
  author={Ryo Yoshida and Mike West},
  journal={Journal of machine learning research : JMLR},
  year={2010},
  volume={11},
  pages={1771-1798}
}
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… CONTINUE READING
Highly Cited
This paper has 22 citations. REVIEW CITATIONS