Discrete Component Analysis

Details Discrete Component Analysis Discrete Component Analysis

2006-04-18

arxiv.org/abs/math/0604410v1

Lecture Notes in Computer Science. Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives

Mathematics

Statistics Theory

Scientific paper

10.1007/11752790_1

This article presents a unified theory for analysis of components in discrete data, and compares the methods with techniques such as independent component analysis, non-negative matrix factorisation and latent Dirichlet allocation. The main families of algorithms discussed are a variational approximation, Gibbs sampling, and Rao-Blackwellised Gibbs sampling. Applications are presented for voting records from the United States Senate for 2003, and for the Reuters-21578 newswire collection.

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