Uncertainty in Artificial Intelligence
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Tighter Linear Program Relaxations for High Order Graphical Models
Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss
Graphical models with High Order Potentials (HOPs) have received considerable interest in recent years. While there are a variety of approaches to inference in these models, nearly all of them amount to solving a linear program (LP) relaxation with unary consistency constraints between the HOP and the individual variables. In many cases, the resulting relaxations are loose, and in these cases the results of inference can be poor. It is thus desirable to look for more accurate ways of performing inference in these models. In this work, we study the LP relaxations that result from enforcing additional consistency constraints between the HOP and the rest of the model. We address theoretical questions about the strength of the resulting relaxations compared to the relaxations that arise in standard approaches, and we develop practical and efficient message passing algorithms for optimizing the LPs. Empirically, we show that the LPs with additional consistency constraints lead to more accurate inference on some challenging problems that include a combination of low order and high order terms.
Pages: 421-430
PS Link:
PDF Link: /papers/13/p421-mezuman.pdf
AUTHOR = "Elad Mezuman and Daniel Tarlow and Amir Globerson and Yair Weiss",
TITLE = "Tighter Linear Program Relaxations for High Order Graphical Models",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "421--430"

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