Clustering A Collection of Networks With Mixtures of L1-Sparse Graphical Models

Zuogong Yue, Victor Solo

Abstract: Attention has turned in recently to clustering of collections networks where each network is characterized by a sparse inverse covariance matrix. Here we consider a mixture model of sparse Gaussian graphical models and develop an exact EM algorithm that improves considerably on a previous approximation. We provide numerical examples to show its superior performance.