Discrete Component Analysis
Abstract
Description
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.
Keywords
Citation
Consulte el texto completo en el siguiente enlace:
https://arxiv.org/abs/math/0604410
http://arxiv.org/abs/math/0604410
Lecture Notes in Computer Science. Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers
doi:10.1007/11752790_1
http://arxiv.org/abs/math/0604410
Lecture Notes in Computer Science. Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers
doi:10.1007/11752790_1