Uncertainty in Artificial Intelligence
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Accelerating EM: An Empirical Study
Luis Ortiz, Leslie Kaelbling
Abstract:
Many applications require that we learn the parameters of a model from data. EM is a method used to learn the parameters of probabilistic models for which the data for some of the variables in the models is either missing or hidden. There are instances in which this method is slow to converge. Therefore, several accelerations have been proposed to improve the method. None of the proposed acceleration methods are theoretically dominant and experimental comparisons are lacking. In this paper, we present the different proposed accelerations and try to compare them experimentally. From the results of the experiments, we argue that some acceleration of EM is always possible, but that which acceleration is superior depends on properties of the problem.
Keywords: earning, hidden variables, Expectation-Maximization, gradient-based methods, Mixtur
Pages: 512-521
PS Link: http://www.cs.brown.edu/people/leo/papers/emvcg_tr.ps.gz
PDF Link: /papers/99/p512-ortiz.pdf
BibTex:
@INPROCEEDINGS{Ortiz99,
AUTHOR = "Luis Ortiz and Leslie Kaelbling",
TITLE = "Accelerating EM: An Empirical Study",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "512--521"
}


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