NDLI: Maximum weight matching via max-product belief propagation

Content Provider	IEEE Xplore Digital Library
Author	Bayati, M. Shah, D. Sharma, M.
Copyright Year	2005
Description	Author affiliation: Dept. of EE, Stanford Univ., CA (Bayati, M.)
Abstract	The max-product "belief propagation" algorithm is an iterative, local, message passing algorithm for finding the maximum a posteriori (MAP) assignment of a discrete probability distribution specified by a graphical model. Despite the spectacular success of the algorithm in many application areas such as iterative decoding and computer vision which involve graphs with many cycles, theoretical convergence results are only known for graphs which are tree-like or have a single cycle. In this paper, we consider a weighted complete bipartite graph and define a probability distribution on it whose MAP assignment corresponds to the maximum weight matching (MWM) in that graph. We analyze the fixed points of the max-product algorithm when run on this graph and prove the surprising result that even though the underlying graph has many short cycles, the maxproduct assignment converges to the correct MAP assignment. We also provide a bound on the number of iterations required by the algorithm
Starting Page	1763
Ending Page	1767
File Size	366012
Page Count	5
File Format	PDF
ISBN	0780391519
DOI	10.1109/ISIT.2005.1523648
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-04
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Belief propagation Iterative algorithms Probability distribution Message passing Graphical models Application software Iterative decoding Computer vision Convergence Tree graphs
Content Type	Text
Resource Type	Article

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