Efficient Neighborhood Selection for Gaussian Graphical Models
Machine Learning
2015-09-23 v1 Information Theory
Machine Learning
math.IT
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.
Cite
@article{arxiv.1509.06449,
title = {Efficient Neighborhood Selection for Gaussian Graphical Models},
author = {Yingxiang Yang and Jalal Etesami and Negar Kiyavash},
journal= {arXiv preprint arXiv:1509.06449},
year = {2015}
}