A note on variational Bayesian factor analysis.
Existing works on variational bayesian (VB) treatment for factor analysis (FA) model such as [Ghahramani, Z., & Beal, M. (2000). Variational inference for Bayesian mixture of factor analysers. In Advances in neural information proceeding systems. Cambridge, MA: MIT Press; Nielsen, F. B. (2004). Variational approach to factor analysis and related models. Master's thesis, The Institute of Informatics and Mathematical Modelling, Technical University of Denmark.] are found theoretically and empirically to suffer two problems: (1) penalize the model more heavily than BIC and (2) perform unsatisfactorily in low noise cases as redundant factors can not be effectively suppressed. A novel VB treatment is proposed in this paper to resolve the two problems and a simulation study is conducted to testify its improved performance over existing treatments.
Zhao
Jian-hua
JH
Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong. jhzhao.ynu@gmail.com
Yu
Philip L H
PL
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated