A new look at reweighted message passing
Kolmogorov V. 2015. A new look at reweighted message passing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(5), 919–930.
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Abstract
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.
Publishing Year
Date Published
2015-05-01
Journal Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
37
Issue
5
Page
919 - 930
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Kolmogorov V. A new look at reweighted message passing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015;37(5):919-930. doi:10.1109/TPAMI.2014.2363465
Kolmogorov, V. (2015). A new look at reweighted message passing. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2014.2363465
Kolmogorov, Vladimir. “A New Look at Reweighted Message Passing.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2015. https://doi.org/10.1109/TPAMI.2014.2363465.
V. Kolmogorov, “A new look at reweighted message passing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5. IEEE, pp. 919–930, 2015.
Kolmogorov V. 2015. A new look at reweighted message passing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(5), 919–930.
Kolmogorov, Vladimir. “A New Look at Reweighted Message Passing.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, IEEE, 2015, pp. 919–30, doi:10.1109/TPAMI.2014.2363465.
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