Discussion: Latent variable graphical model selection via convex optimization

DOI10.1214/12-AOS979zbMath1288.62092arXiv1211.0801MaRDI QIDQ1940763

Ming Yuan

Publication date: 7 March 2013

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1211.0801

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Applications of graph theory (05C90) Convex programming (90C25)

Related Items (3)

Adjusted regularization in latent graphical models: application to multiple-neuron spike count data ⋮ Adjusted regularization of cortical covariance ⋮ Psychometric network models from time-series and panel data

Uses Software

• SDPT3
• YALMIP
• PMA

Cites Work

• A well-conditioned estimator for large-dimensional covariance matrices
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• Gaussian Markov distributions over finite graphs
• Characterization of the subdifferential of some matrix norms
• On the distribution of the largest eigenvalue in principal components analysis
• Rejoinder: Latent variable graphical model selection via convex optimization
• Sparse permutation invariant covariance estimation
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• Regularized estimation of large covariance matrices
• High-dimensional graphs and variable selection with the Lasso
• Exact matrix completion via convex optimization
• Nonparametric estimation of large covariance matrices of longitudinal data
• Robust principal component analysis?
• Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm
• Rank-Sparsity Incoherence for Matrix Decomposition
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• SDPT3 — A Matlab software package for semidefinite programming, Version 1.3
• Equivalent Subgradient Versions of Hamiltonian and Euler–Lagrange Equations in Variational Analysis
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Compressed sensing
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