2012/06/18 by Patrick Pletscher, Pletscher, Patrick, Sharon Wulff +1
Computer Science · Mathematics · #Error Correcting Code Techniques #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1206.4681
ICML2012
arxiv created 2012/06/18 · arxiv updated 2012/06/22
MAP inference for general energy functions remains a challenging problem. While most efforts are channeled towards improving the linear programming (LP) based relaxation, this work is motivated by the quadratic programming (QP) relaxation. We propose a novel MAP relaxation that penalizes the Kullback-Leibler divergence between the LP pairwise auxiliary variables, and QP equivalent terms given by the product of the unaries. We develop two efficient algorithms based on variants of this relaxation. The algorithms minimize the non-convex objective using belief propagation and dual decomposition as building blocks. Experiments on synthetic and real-world data show that the solutions returned by our algorithms substantially improve over the LP relaxation.