Discrete Component Analysis

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dc.creatorBuntine, Wray
dc.creatorJakulin, Aleks
dc.date2006-04-18
dc.date.accessioned2026-07-07T08:07:44Z
dc.date.available2026-07-07T08:07:44Z
dc.descriptionThis 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.
dc.identifierhttps://arxiv.org/abs/math/0604410
dc.identifierhttp://arxiv.org/abs/math/0604410
dc.identifierLecture 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
dc.identifierdoi:10.1007/11752790_1
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131031
dc.subjectStatistics Theory
dc.subject62F15; 68T50; 62P25
dc.titleDiscrete Component Analysis
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