Discussion: Latent variable graphical model selection via convex optimization

2012/08/01 by Christophe Giraud, Alexandre B. Tsybakov, Alexandre Tsybakov
Computer Science · Engineering · Mathematics · #Artificial intelligence #Bayesian Methods and Mixture Models #Computer science #Control Systems and Identification #Geometry #Graphical model #Latent class model #Latent variable #Latent variable model #Machine learning #Mathematical economics #Mathematical optimization #Mathematics #Regular polygon #Selection (genetic algorithm) #Statistical Methods and Inference #Variable (mathematics) #math.ST #stat.TH

paper · pdf · doi:10.1214/12-aos984

published as Annals of Statistics 2012, Vol. 40, No. 4, 1984-1988 · Published in at http://dx.doi.org/10.1214/12-AOS984 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

openalex publication_date 2012/08/01 · arxiv created 2012/11/05 · arxiv updated 2012/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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

Citations

• Latent variable graphical model selection via convex optimization
• A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
• How Hard Is It to Approximate the Best Nash Equilibrium?
• Sparse Matrix Inversion with Scaled Lasso
• Robust principal component analysis?