Online k-MLE for Mixture Modeling with Exponential Families

被引：0

作者：

Saint-Jean, Christophe ^{[1
]}

Nielsen, Frank ^{[2
]}

机构：

[1] Univ La Rochelle, MIA, La Rochelle, France

[2] Ecole Polytech, LIX, Palaiseau, France

来源：

GEOMETRIC SCIENCE OF INFORMATION, GSI 2015 | 2015年 / 9389卷

关键词：

Mixture modeling; Online learning; k-MLE; Wishart distribution; CLASSIFICATION EM ALGORITHM;

D O I：

10.1007/978-3-319-25040-3_37

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper address the problem of online learning finite statistical mixtures of exponential families. A short review of the Expectation-Maximization (EM) algorithm and its online extensions is done. From these extensions and the description of the k-Maximum Likelihood Estimator (k-MLE), three online extensions are proposed for this latter. To illustrate them, we consider the case of mixtures of Wishart distributions by giving details and providing some experiments.

引用

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页码：340 / 348

页数：9

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