Uncertainty in Artificial Intelligence
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The Information Bottleneck EM Algorithm
Gal Elidan, Nir Friedman
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new approach that is based on the Information Bottleneck principle. In this approach, we view the learning problem as a tradeoff between two information theoretic objectives. The first is to make the hidden variables uninformative about the identity of specific instances. The second is to make the hidden variables informative about the observed attributes. By exploring different tradeoffs between these two objectives, we can gradually converge on a high-scoring solution. As we show, the resulting, Information Bottleneck Expectation Maximization (IB-EM) algorithm, manages to find solutions that are superior to standard EM methods.
Keywords: Bayesian networks, Hidden variables, Information Bottleneck, Continuation
Pages: 200-208
PS Link: http://www.cs.huji.ac.il/~galel/papers/ibem.ps
PDF Link: /papers/03/p200-elidan.pdf
AUTHOR = "Gal Elidan and Nir Friedman",
TITLE = "The Information Bottleneck EM Algorithm",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "200--208"

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