A global approach for learning sparse Ising models.

We consider the problem of learning the link parameters as well as the structure of a binary-valued pairwise Markov model. We propose a method based on $l_1$- regularized logistic regression, which estimate globally the whole set of edges and link parameters. Unlike the more recent methods discussed in literature that learn the edges and the corresponding link parameters one node at a time, in this work we propose a method that learns all the edges and corresponding link parameters simultaneously for all nodes, in a global manner. The idea behind this proposal is to exploit the reciprocal information of the nodes between each other during the estimation process. Detailed numerical experiments highlight the advantage of this technique and confirm the intuition behind it.