Entropy Adjusted Graphical Lasso for Sparse Precision Matrix Estimation

TL;DR

This paper introduces an entropy-adjusted Graphical Lasso method that incorporates a log-determinant penalty to improve sparse precision matrix estimation by explicitly managing uncertainty, demonstrated through extensive simulations and real data.

Contribution

It proposes a novel extension of Graphical Lasso with an entropy adjustment via a log-determinant penalty, enhancing sparsity and uncertainty control in precision matrix estimation.

Findings

Improved accuracy in sparse precision matrix estimation.

Better uncertainty management compared to standard methods.

Enhanced performance on both simulated and real datasets.

Abstract

The estimation of a precision matrix is a crucial problem in various research fields, particularly when working with high dimensional data. In such settings, the most common approach is to use the penalized maximum likelihood. The literature typically employs Lasso, Ridge and Elastic Net norms, which effectively shrink the entries of the estimated precision matrix. Although these shrinkage approaches provide well-conditioned precision matrix estimates, they do not explicitly address the uncertainty associated with these estimated matrices. In fact, as the matrix becomes sparser, the precision matrix imposes fewer restrictions, leading to greater variability in the distribution, and thus, to higher entropy. In this paper, we introduce an entropy-adjusted extension of widely used Graphical Lasso using an additional log-determinant penalty term. The objective of the proposed technique is to impose sparsity on the precision matrix estimate and adjust the uncertainty through the log-determinant term. The advantage of the proposed method compared to the existing ones in the literature is evaluated through comprehensive numerical analyses, including both simulated and real-world datasets. The results demonstrate its benefits compared to existing approaches in the literature, with respect to several evaluation metrics.