PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Structured Prediction, Dual Extragradient and Bregman Projections
Ben Taskar, Simon Lacoste-Julien and Michael I. Jordan
Journal of Machine Learning Research (JMLR) - Special Topic on Machine Learning and Optimization Volume 7, pp. 1627-1653, 2006. ISSN 1533-7928

Abstract

We present a simple and scalable algorithm for maximum-margin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convex-concave saddle-point problem that allows us to use simple projection methods based on the dual extragradient algorithm (Nesterov, 2003). The projection step can be solved using dynamic programming or combinatorial algorithms for min-cost convex flow, depending on the structure of the problem. We show that this approach provides a memory-efficient alternative to formulations based on reductions to a quadratic program (QP). We analyze the convergence of the method and 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:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Natural Language Processing
Theory & Algorithms
ID Code:5338
Deposited By:Simon Lacoste-Julien
Deposited On:24 March 2009