BibTeX
@TECHREPORT{Buntine05discreteprincipal,
author = {Wray Buntine and Aleks Jakulin},
title = {Discrete principal component analysis},
institution = {},
year = {2005}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Abstract. This article presents a unified theory for analysis of components in discrete data, and compares the methods with techniques such as independent component analysis (ICA), non-negative matrix factorisation (NMF) and latent Dirichlet allocation (LDA). The main families of algorithms discussed are mean field, Gibbs sampling, and Rao-Blackwellised Gibbs sampling. Applications are presented for voting records from the United States Senate for 2003, and the use of components in subsequent classification.
