Graphical Exponential Screening
Machine Learning
2016-07-05 v2
Abstract
In high dimensions we propose and analyze an aggregation estimator of the precision matrix for Gaussian graphical models. This estimator, called graphical Exponential Screening (gES), linearly combines a suitable set of individual estimators with different underlying graphs, and balances the estimation error and sparsity. We study the risk of this aggregation estimator and show that it is comparable to that of the best estimator based on a single graph, chosen by an oracle. Numerical performance of our method is investigated using both simulated and real datasets, in comparison with some state-of-art estimation procedures.
Keywords
Cite
@article{arxiv.1503.02698,
title = {Graphical Exponential Screening},
author = {Zhe Liu},
journal= {arXiv preprint arXiv:1503.02698},
year = {2016}
}