A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction

In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters efficiently. We first relate CRFs and structured SVMs and show that in CRFs a variant of the log-partition function, known as the soft-max, smoothly approximates the hinge loss function of structured SVMs. We then propose an intuitive approximation for the structured prediction problem, using duality, based on a local entropy approximation and derive an efficient messagepassing algorithm that is guaranteed to converge. Unlike existing approaches, this allows us to learn efficiently graphical models with cycles and very large number of parameters.