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 $\ell_{2,1}$-norm regularized maximum likelihood estimators to learn the model parameters from i.i.d.