PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Structured Prediction via the Extragradient Method
Ben Taskar, Simon Lacoste-Julien and Michael I. Jordan
In: NIPS 2005, 5-8 Dec 2005, Vancouver, BC, Canada.


We present a simple and scalable algorithm for large-margin estimation of structured models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convex-concave saddle-point problem and apply the extragradient method, yielding an algorithm with linear convergence using simple gradient and projection calculations. The projection step can be solved using combinatorial algorithms for min-cost quadratic flow. This makes the approach an efficient alternative to formulations based on reductions to a quadratic program (QP). We present experiments on two very different structured prediction tasks: 3D image segmentation and word alignment, illustrating the favorable scaling properties of our algorithm.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Natural Language Processing
Theory & Algorithms
ID Code:5337
Deposited By:Simon Lacoste-Julien
Deposited On:24 March 2009