Discussion: Latent variable graphical model selection via convex optimization

Abstract

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

arXiv commentPublished in at http://dx.doi.org/10.1214/12-AOS981 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (5)

• High-dimensional graphs and variable selection with the Lasso
• Rank-Sparsity Incoherence for Matrix Decomposition
• Sparse permutation invariant covariance estimation
• Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
• Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting

Cited by in corpus (3)

• Schrödinger PCA: On the Duality between Principal Component Analysis and Schrödinger Equation
• Ising Models with Latent Conditional Gaussian Variables
• Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity