TY -
TI - Bayes PCA Revisited
LA - eng
PB - Museum Tusculanum
AU - Sporring, Jon
PY - 2008
SN - 01078283
AB - Principle Component Analysis is a simple tool to obtain linear models for stochastic data and is used both for a data reduction or equivalently noise elim- ination and for data analysis. Principle Component Analysis ts a multivariate Gaussian distribution to the data, and the typical method is by using the log- likelihood estimator. However for small sets of high dimensional data, the log- likelihood estimator is often far from convergence, and therefore reliable models must be obtained by use of prior information. In this paper, we will examine an earlier work on reconstructing missing data using statistical knowledge and regularization, we will show the circumstances for which this is equivalent to a Bayes estimation, we will give an expository presentation of Bayes Principle Component Analysis for a range of exponential type priors, and we will develop algorithms for their estimate.
ER -