Efficient Neighborhood Selection for Gaussian Graphical Models
Yingxiang Yang, Jalal Etesami, Negar Kiyavash · arXiv (Cornell University) · 2015
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.