Learning Mixed Graphical Models
This addresses the challenge of modeling mixed data types in graphical models, which is incremental as it builds on prior work for homogeneous data.
The paper tackles the problem of learning the structure of pairwise graphical models that include both continuous and discrete variables, presenting a new model that generalizes existing Gaussian and discrete methods with a novel penalization scheme using group-lasso.
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.