Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

DOI10.48550/ARXIV.1710.11223arXiv1710.11223MaRDI QIDQ149683

Author name not available (Why is that?)

Publication date: 30 October 2017

Abstract: We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via an Elementary Estimator under a high-dimensional situation. DIFFEE is solved through a faster and closed form solution that enables it to work in large-scale settings. We conduct a rigorous statistical analysis showing that surprisingly DIFFEE achieves the same asymptotic convergence rates as the state-of-the-art estimators that are much more difficult to compute. Our experimental results on multiple synthetic datasets and one real-world data about brain connectivity show strong performance improvements over baselines, as well as significant computational benefits.

This page was built for publication: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q149683)