Sparse Gaussian graphical model estimation via alternating minimization

    loading  Checking for direct PDF access through Ovid

Abstract

Several methods have recently been proposed for estimating sparse Gaussian graphical models using Symbol-regularization on the inverse covariance or precision matrix. Despite recent advances, contemporary applications require even faster methods to handle ill-conditioned high-dimensional datasets. In this paper, we propose a new method for solving the sparse inverse covariance estimation problem using the alternating minimization algorithm, which effectively works as a proximal gradient algorithm on the dual problem. Our approach has several advantages: it is faster than state-of-the-art algorithms by many orders of magnitude; its global linear convergence has been rigorously demonstrated, underscoring its good theoretical properties; it facilitates additional constraints on pairwise or marginal relationships between feature pairs based on domain-specific knowledge; and it is better at handling extremely ill-conditioned problems. Our algorithm is shown to be more accurate and faster on simulated and real datasets.

Related Topics

    loading  Loading Related Articles