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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, 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

Independent Component Analysis Gibbs Sampling Latent Dirichlet Allocation Independent Component Analysis Latent Semantic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wray Buntine
    • 1
  • Aleks Jakulin
    • 2
  1. 1.Helsinki Institute for Information Technology (HIIT), Dept. of Computer ScienceUniversity of HelsinkiFinland
  2. 2.Department of Knowledge TechnologiesJozef Stefan InstituteLjubljanaSlovenia

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