Latent variable graphical model selection via convex optimization
نویسندگان
چکیده
منابع مشابه
Of ” Latent Variable Graphical Model Selection via Convex Optimization
Since recently, there have been an increasing interest in the problem of estimating a high-dimensional matrix K that can be decomposed in a sum of a sparse matrix S∗ (i.e., a matrix having only a small number of nonzero entries) and a low rank matrix L∗. This is motivated by applications in computer vision, video segmentation, computational biology, semantic indexing etc. The main contribution ...
متن کاملLatent Variable Graphical Model Selection via Convex Optimization
I want to start by congratulating Professors Chandrasekaran, Parrilo and Willsky for this fine piece of work. Their paper, hereafter referred to as CPW, addresses one of the biggest practical challenges of Gaussian graphical models—how to make inferences for a graphical model in the presence of missing variables. The difficulty comes from the fact that the validity of conditional independence r...
متن کاملDiscussion : Latent Variable Graphical Model Selection via Convex Optimization
1. Introduction. We would like to congratulate the authors for their refreshing contribution to this high-dimensional latent variables graphical model selection problem. The problem of covariance and concentration matrices is fundamentally important in several classical statistical methodolo-gies and many applications. Recently, sparse concentration matrices estimation had received considerable...
متن کاملRejoinder: Latent variable graphical model selection via convex optimization
1. Introduction. We thank all the discussants for their careful reading of our paper, and for their insightful critiques. We would also like to thank the editors for organizing this discussion. Our paper contributes to the area of high-dimensional statistics which has received much attention over the past several years across the statistics, machine learning and signal processing communities. I...
متن کاملLatent Variable Graphical Model Selection via Convex Optimization – Supplementary
1. Matrix perturbation bounds. Given a low-rank matrix we consider what happens to the invariant subspaces when the matrix is perturbed by a small amount. We assume without loss of generality that the matrix under consideration is square and symmetric, and our methods can be extended to the general non-symmetric non-square case. We refer the interested reader to [1, 3] for more details, as the ...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2012
ISSN: 0090-5364
DOI: 10.1214/11-aos949