On Improving The Efficiency of The Iterative Proportional Fitting Procedure

Abstract

Iterative proportional fitting (IPF) on junction trees is an important tool for learning in graphical models. We identify the propagation and IPF updates on the junction tree as fixed point equations of a single constrained entropy maximization problem. This allows a more efficient message updating protocol than the well known effective IPF of Jiroušek and Preučil (1995). When the junction tree has an intractably large maximum clique size we propose to maximize an approximate constrained entropy based on region graphs (Yedidia et al., 2002). To maximize the new objective we propose a "loopy" version of IPF. We show that this yields accurate estimates of the weights of undirected graphical models in a simple experiment.