Date of Original Version

2006

Type

Conference Proceeding

Published In

International Conference on Machine Learning 2006, May, 2006

Rights Management

http://doi.acm.org/10.1145/1143844.1143913

Abstract or Table of Contents

We present an efficient method for maximizing energy functions with first and second order potentials, suitable for MAP labeling estimation problems that arise in undirected graphical models. Our approach is to relax the integer constraints on the solution in two steps. First we efficiently obtain the relaxed global optimum following a procedure similar to the iterative power method for finding the largest eigenvector of a matrix. Next, we map the relaxed optimum on a simplex and show that the new energy obtained has a certain optimal bound. Starting from this energy we follow an efficient coordinate ascent procedure that is guaranteed to increase the energy at every step and converge to a solution that obeys the initial integral constraints. We also present a sufficient condition for ascent procedures that guarantees the increase in energy at every step.

Comments

Copyright © 2006 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. © ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the Proceedings of the 23rd international conference on Machine learning {1-59593-383-2 (2006)} http://doi.acm.org/10.1145/1143844.1143913

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