Quadratic programming relaxations for metric labeling and Markov random field MAP estimation

Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem. An additional convex relaxation of the quadratic approximation is shown to have additive approximation guarantees that apply even when the graph weights have mixed sign or do not come from a metric. The approximations are extended in a manner that allows tight variational relaxations of the MAP problem, although they generally involve non-convex optimization. Experiments carried out on synthetic data show that the quadratic approximations can be more accurate and computationally efficient than the linear programming and propagation based alternatives.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Pradeep D. Ravikumar, John D. Lafferty
• Keywords: ICML 2006, Machine Learning, Quadratic Approximations, Quadratic Program Relaxations, Tight Variational Relaxations