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Estimating Dependency Structure as a Hidden Variable

Author(s)
Meila, Marina; Jordan, Michael I.; Morris, Quaid
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Abstract
This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.
Date issued
1998-09-01
URI
http://hdl.handle.net/1721.1/7257
Other identifiers
AIM-1648
CBCL-165
Series/Report no.
AIM-1648CBCL-165

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  • AI Memos (1959 - 2004)
  • CBCL Memos (1993 - 2004)

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