Model Selection Through Sparse Maximum Likelihood Estimation

Understand

We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse.

• Our approach is to solve a maximum likelihood problem with an added l_1-norm penalty term.
• The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes.
• We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case.