Markov Random Field MAP as Set Partitioning
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Title of host publication | Proceedings of Machine Learning Research |
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Date | Published - 2018 |
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Pages | 85-96 |
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Number of pages | 12 |
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Volume | 72 |
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Original language | English |
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The Markov Random Field (MRF) MAP inference problem is considered from the viewpoint of
integer programming (IP). The problem is shown to be a (pure) set partitioning problem (SPP). This
allows us to bring existing work on SPP to bear on the MAP problem. Facets (maximally strong
linear inequalities) of the closely related set packing (SP) problem are shown to be useful for MRF
MAP. These facets include odd hole and odd anti-hole inequalities which are shown to be findable
using a zero-half cut generator. Experimental results using CPLEX show that for MRF MAP
problems, generating more zero-half cuts than normal typically brings performance improvements.
Pre-processing methods to reduce the size of MRF MAP problems are also considered, and some
preliminary results on their usefulness presented.
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