Applying Discrete PCA in Data Analysis

Wray Buntine

Helsinki Inst. of Information Technology
HIIT, P.O. Box 9800
FIN-02015 HUT, Finland

Alex Jakulin

Faculty of Computer and Information Science
University of Ljubljana
Trzaska 25, SI-1001, Ljubljana, Slovenia


Appeared in Uncertainty in AI, 2004. PDF version.

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Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some application of these methods to some common statistical tasks. We show that these methods can be interpreted as a discrete version of ICA. We develop a hierarchical version yielding components at different levels of detail, and additional techniques for Gibbs sampling. We compare the algorithms on a text prediction task using support vector machines, and to information retrieval.

Wray Buntine
Last modified: Fri May 28 11:08:57 EEST 2004