Efficient MAP approximation for dense energy functions

We present an efficient method for maximizing energy functions with first and second order potentials, suitable for MAP labeling estimation problems that arise in undirected graphical models. Our approach is to relax the integer constraints on the solution in two steps. First we efficiently obtain the relaxed global optimum following a procedure similar to the iterative power method for finding the largest eigenvector of a matrix. Next, we map the relaxed optimum on a simplex and show that the new energy obtained has a certain optimal bound. Starting from this energy we follow an efficient coordinate ascent procedure that is guaranteed to increase the energy at every step and converge to a solution that obeys the initial integral constraints. We also present a sufficient condition for ascent procedures that guarantees the increase in energy at every step.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Marius Leordeanu, Martial Hebert
• Keywords: Coordinate Ascent Procedure, ICML 2006, Initial Integral Constraints, Machine Learning, Relaxed Optimum