Abstract
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. Copyright © 2008 Elsevier Ltd. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 988-997 |
| Journal | Neural Networks |
| Volume | 22 |
| Issue number | 7 |
| Early online date | Nov 2008 |
| DOIs | |
| Publication status | Published - Sept 2009 |
Keywords
- Factor analysis
- VB
- BIC
Fingerprint
Dive into the research topics of 'A note on variational Bayesian factor analysis'. Together they form a unique fingerprint.- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS