Sparse Quadratic Approximation for Graph Learning
Description
Learning graphs represented by M-matrices via an l(1)-regularized Gaussian maximum-likelihood method is a popular approach, but also one that poses computational challenges for large scale datasets. Recently proposed methods cast this problem as a
