PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exponential Family Graph Matching and Ranking
James Petterson, Tiberio Caetano, Julian McAuley and Jin Yu
In: NIPS 2009, 6-11 Dec 2009, Vancouver, Canada.

Abstract

We present a method for learning max-weight matching predictors in bipartite graphs. The method consists of performing maximum a posteriori estimation in exponential families with sufficient statistics that encode permutations and data features. Although inference is in general hard, we show that for one very relevant application–document ranking–exact inference is efficient. For general model instances, an appropriate sampler is readily available. Contrary to existing max-margin matching models, our approach is statistically consistent and, in addition, experiments with increasing sample sizes indicate superior improvement over such models. We apply the method to graph matching in computer vision as well as to a standard benchmark dataset for learning document ranking, in which we obtain state-of-the-art results, in particular improving on max-margin variants. The drawback of this method with respect to max-margin alternatives is its run-time for large graphs, which is comparatively high.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:5683
Deposited By:James Petterson
Deposited On:08 March 2010