Automatic PCA Dimension Selection for High Dimensional Data and Small Sample Sizes

Bayesian inference from high-dimensional data involves the integration over a large number of model parameters. Accurate evaluation of such high-dimensional integrals raises a unique set of issues. These issues are illustrated using the exemplar of model selection for principal component analysis (PCA). A Bayesian model selection criterion, based on a… (More)