### 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 language | English |
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Title of host publication | Proceedings of the Sixth SIAM International Conference on Data Mining |

Pages | 519-523 |

Number of pages | 5 |

Publication status | Published - 2006 Jul 3 |

Event | Sixth SIAM International Conference on Data Mining - Bethesda, MD, United States Duration: 2006 Apr 20 → 2006 Apr 22 |

### Publication series

Name | Proceedings of the Sixth SIAM International Conference on Data Mining |
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Volume | 2006 |

### Conference

Conference | Sixth SIAM International Conference on Data Mining |
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Country | United States |

City | Bethesda, MD |

Period | 06/4/20 → 06/4/22 |

### Keywords

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

### ASJC Scopus subject areas

- Engineering(all)

## Cite this

Fujimoto, Y., & Murata, N. (2006). Robust estimation for mixture of probability tables based on β-likelihood. In

*Proceedings of the Sixth SIAM International Conference on Data Mining*(pp. 519-523). (Proceedings of the Sixth SIAM International Conference on Data Mining; Vol. 2006).