Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation

We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms. We propose two ℓ2,1norm regularized maximum likelihood estimators to learn the model parameters from i.i.d.