PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An LP View of the M-best MAP problem
Menachem Fromer and Amir Globerson
Advances in Neural Information Processing Systems 22 pp. 567-575, 2009.

Abstract

We consider the problem of finding the M assignments with maximum probability in a probabilistic graphical model. We show how this problem can be formulated as a linear program (LP) on a particular polytope. We prove that, for tree graphs (and junction trees in general), this polytope has a particularly simple form and differs from the marginal polytope in a single inequality constraint. We use this characterization to provide an approximation scheme for non-tree graphs, by using the set of spanning trees over such graphs. The method we present puts the M-best inference problem in the context of LP relaxations, which have recently received considerable attention and have proven useful in solving difficult inference problems. We show empirically that our method often finds the provably exact M best configurations for problems of high tree-width.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5518
Deposited By:Amir Globerson
Deposited On:13 January 2010