Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials

Previous work has examined structure learning in log-linear models with `1regularization, largely focusing on the case of pairwise potentials. In this work we consider the case of models with potentials of arbitrary order, but that satisfy a hierarchical constraint. We enforce the hierarchical constraint using group `1-regularization with overlapping groups. An active set method that enforces hierarchical inclusion allows us to tractably consider the exponential number of higher-order potentials. We use a spectral projected gradient method as a subroutine for solving the overlapping group `1regularization problem, and make use of a sparse version of Dykstra's algorithm to compute the projection. Our experiments indicate that this model gives equal or better test set likelihood compared to previous models.