Efficient Neighborhood Selection for Gaussian Graphical Models

Abstract

This paper addresses the problem of neighborhood selection for Gaussian graphical models. We present two heuristic algorithms: a forward-backward greedy algorithm for general Gaussian graphical models based on mutual information test, and a threshold-based algorithm for walk summable Gaussian graphical models. Both algorithms are shown to be structurally consistent, and efficient. Numerical results show that both algorithms work very well.

References in corpus (1)

• High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods