A New Look at Reweighted Message Passing
A New Look at Reweighted Message Passing
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DOI：
10.1109/tpami.2014.2363465
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发表时间：
2015-05-01
影响因子：
23.6
通讯作者：
Kolmogorov, Vladimir
中科院分区：
文献类型：
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作者：
Kolmogorov, Vladimir
我们提出了一个新的家庭的MAP估计的图形模型，我们称之为顺序重新加权消息传递（SRMP）的消息传递技术。特殊情况包括众所周知的技术，如最小和扩散（MSD）和更快的顺序树重加权消息传递（TRW-S）。重要的是，我们的推导比TRW-S的原始推导更简单，并且不涉及分解成树。这使得容易概括。新的算法家族可以被看作是TRW-S从成对到高阶图形模型的推广。我们测试SRMP在几个现实世界的问题，有希望的结果。
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.