Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations

NeurIPS 2017 Pan XuJian MaQuanquan Gu

We study the estimation of the latent variable Gaussian graphical model (LVGGM), where the precision matrix is the superposition of a sparse matrix and a low-rank matrix. In order to speed up the estimation of the sparse plus low-rank components, we propose a sparsity constrained maximum likelihood estimator based on matrix factorization, and an efficient alternating gradient descent algorithm with hard thresholding to solve it... (read more)

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