title: Learning the Structure of Mixed Graphical Models.
abstract: We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme involves a novel symmetric use of the group-lasso norm and follows naturally from a particular parametrization of the model. Supplementary materials for this paper are available online.
authors.affiliation: Institute of Computational and Mathematical Engineering, Stanford University.
authors.forename: Jason D
authors.initials: JD
authors.lastname: Lee
authors.affiliation: Department of Statistics, Stanford University. ; An earlier version of this paper was published at AISTATS 2013. This version is significantly expanded with new experimental results, comparisons, and theoretical results.
authors.forename: Trevor J
authors.initials: TJ
authors.lastname: Hastie