This paper presents an algorithm for order reduction of
factors in High-Order Markov Random Fields (HOMRFs).
Standard techniques for transforming arbitrary high-order
factors into pairwise ones have been known for a long time.
In this work, we take a fresh look at this problem with the
following motivation: It is important to keep in mind that
order reduction is followed by an inference procedure on
the order-reducedMRF. Since there are many possible ways
of performing order reduction, a technique that generates
“easier” pairwise inference problems is a better reduction.
With this motivation in mind, we introduce a new algorithm
called Order Reduction Inference (ORI) that searches
over a space of order reductionmethods to minimize the difficulty
of the resultant pairwise inference problem. We set
up this search problem as an energy minimization problem.
We show that application of ORI for order reduction outperforms
known order reduction techniques both in simulated...
Andrew C. Gallagher, Dhruv Batra, Devi Parikh