Robust estimation for mixture of probability tables based on β-likelihood

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Modeling of a large joint probability table is problematic when its variables have a large number of categories. In such a case, a mixture of simpler probability tables could be a good model. And the estimation of such a large probability table frequently has another problem of data sparseness. When constructing mixture models with sparse data, EM estimators based on the β-likelihood are expected more appropriate than those based on the log likelihood. Experimental results show that a mixture model estimated by the βlikelihood approximates a large joint probability table with sparse data more appropriately than EM estimators.

Original languageEnglish
Title of host publicationProceedings of the Sixth SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics
Pages519-523
Number of pages5
ISBN (Print)089871611X, 9780898716115
DOIs
Publication statusPublished - 2006
EventSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
Duration: 2006 Apr 202006 Apr 22

Publication series

NameProceedings of the Sixth SIAM International Conference on Data Mining
Volume2006

Conference

ConferenceSixth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityBethesda, MD
Period06/4/2006/4/22

Keywords

  • EM estimation
  • Mixture model
  • Robustness
  • Sparse data
  • β-likelihood

ASJC Scopus subject areas

  • Engineering(all)

Cite this