A New Look at Reweighted Message Passing

We propose a new family of message passing techniques for MAP estimation in graphical models which we call Sequential Reweighted Message Passing (SRMP). Special cases include well-known techniques such as Min-Sum Diffusion (MSD) and a faster Sequential Tree-Reweighted Message Passing (TRW-S). Import...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 37; číslo 5; s. 919 - 930
Hlavní autor: Kolmogorov, Vladimir
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Convergence
Graphical models
Labeling
Linear programming
MAP estimation
Message passing
message passing algorithms
Probability distribution
Vectors
MAP estimation
Graphical models
message passing algorithms
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:We propose a new family of message passing techniques for MAP estimation in graphical models which we call Sequential Reweighted Message Passing (SRMP). Special cases include well-known techniques such as Min-Sum Diffusion (MSD) and a faster Sequential Tree-Reweighted Message Passing (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. The new family of algorithms can be viewed as a generalization of TRW-S from pairwise to higher-order graphical models. We test SRMP on several real-world problems with promising results.
Bibliografie:ObjectType-Article-1
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2014.2363465