A block coordinate descent algorithm for sparse Gaussian graphical model inference with laplacian constraints

Dec 1, 2019ยท
Tianyi Liu
Tianyi Liu
,
Minh Trinh-Hoang
,
Yang Yang
,
Marius Pesavento
ยท 0 min read
Abstract
We consider the problem of inferring sparse Gaussian graphical models with Laplacian constraints, which can also be viewed as learning a graph Laplacian such that the observed graph signals are smooth with respect to it. A block coordinate descent algorithm is proposed for the resulting linearly constrained log-determinant maximum likelihood estimation problem with sparse regularization. Simulation results on synthetic data show the efficiency of our proposed algorithm.
Type
Publication
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing