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.
CITATION STYLE
Saint-Jean, C., & Nielsen, F. (2015). Online k-MLE for mixture modeling with exponential families. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9389, pp. 340–348). Springer Verlag. https://doi.org/10.1007/978-3-319-25040-3_37
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