Bayesian principal component analysis with mixture priors
- Title
- Bayesian principal component analysis with mixture priors
- Author
- 김대경
- Keywords
- Probabilistic principal component analysis; Dimension reduction; Probabilistic latent variable model
- Issue Date
- 2010-09
- Publisher
- 한국통계학회
- Citation
- Journal of the Korean Statistical Society, v. 39, NO. 3, Page. 387-396
- Abstract
- A central issue in principal component analysis (PCA) is that of choosing the appropriate number of principal components to be retained. Bishop (1999a) suggested a Bayesian approach for PCA for determining the effective dimensionality automatically on the basis of the probabilistic latent variable model. This paper extends this approach by using mixture priors, in that the choice dimensionality and estimation of principal components are done simultaneously via MCMC algorithm. Also, the proposed method provides a probabilistic measure of uncertainty on PCA, yielding posterior probabilities of all possible cases of principal components. (C) 2010 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
- URI
- https://link.springer.com/article/10.1016/j.jkss.2010.04.001https://repository.hanyang.ac.kr/handle/20.500.11754/183948
- ISSN
- 1226-3192;1876-4231
- DOI
- 10.1016/j.jkss.2010.04.001
- Appears in Collections:
- COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E](과학기술융합대학) > ETC
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML