Learning latent variable Gaussian graphical model for biomolecular network with low sample complexity

DOI10.1155/2016/2078214zbMath1367.92047OpenAlexW2536936056WikidataQ37396771 ScholiaQ37396771MaRDI QIDQ2011725

Quan Liu, Bo Yuan, Yan-Bo Wang

Publication date: 4 August 2017

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/2078214

zbMATH Keywords

sparsityGaussian graphical modeladditive convex regularizationslow rankness

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Systems biology, networks (92C42)

Uses Software

• GADMM

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Latent variable graphical model selection via convex optimization
• Parallel splitting augmented Lagrangian methods for monotone structured variational inequalities
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• A trace inequality of John von Neumann
• Sparse permutation invariant covariance estimation
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• Network exploration via the adaptive LASSO and SCAD penalties
• Strong oracle optimality of folded concave penalized estimation
• Generalized alternating direction method of multipliers: new theoretical insights and applications
• On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
• Robust principal component analysis?
• Joint estimation of multiple graphical models
• Model selection and estimation in the Gaussian graphical model
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• PARTIAL CORRELATION AND CONDITIONAL CORRELATION AS MEASURES OF CONDITIONAL INDEPENDENCE
• Sparsity and Smoothness Via the Fused Lasso
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection
• On first-order algorithms forl₁/nuclear norm minimization
• Model Selection and Estimation in Regression with Grouped Variables
• Local Shrinkage Rules, Lévy Processes and Regularized Regression
• Compressed sensing
• A general theory of concave regularization for high-dimensional sparse estimation problems