@inproceedings{4e3283ea8125407780d0633ba39eadb4,

title = "Robust estimation for mixture of probability tables based on β-likelihood",

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.",

keywords = "EM estimation, Mixture model, Robustness, Sparse data, β-likelihood",

author = "Yu Fujimoto and Noboru Murata",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Sixth SIAM International Conference on Data Mining ; Conference date: 20-04-2006 Through 22-04-2006",

year = "2006",

doi = "10.1137/1.9781611972764.52",

language = "English",

isbn = "089871611X",

series = "Proceedings of the Sixth SIAM International Conference on Data Mining",

publisher = "Society for Industrial and Applied Mathematics",

pages = "519--523",

booktitle = "Proceedings of the Sixth SIAM International Conference on Data Mining",

}