Probabilistic Principal Component Analysis
- M. E. Tipping ,
- Christopher Bishop
Journal of the Royal Statistical Society, Series B | , Vol 21(3): pp. 611-622
Available from http://www.ncrg.aston.ac.uk/Papers/index.html
Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach to PCA.