Efficient Latent Variable Graphical Model Selection via Split Bregman Method

Penalized Bregman Divergence Estimation via Coordinate Descent

Variable selection via penalized estimation is appealing for dimension reduction. For penalized linear regression, Efron, et al. (2004) introduced the LARS algorithm. Recently, the coordinate descent (CD) algorithm was developed by Friedman, et al. (2007) for penalized linear regression and penalized logistic regression and was shown to gain computational superiority. This paper explores...

Rejoinder: Latent Variable Graphical Model Selection via Convex Optimization by Venkat Chandrasekaran,

Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs

Graphical model selection refers to the problem of estimating the unknown graph structure given observations at the nodes in the model. We consider a challenging instance of this problem when some of the nodes are latent or hidden. We characterize conditions for tractable graph estimation and develop efficient methods with provable guarantees. We consider the class of Ising models Markov on loc...

Latent Variable Graphical Model Selection via Convex Optimization1 by Venkat Chandrasekaran,

Suppose we observe samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and observed variables. Is it possible to discover the number of latent components, and to learn a statistical model over the entire collection of variables? We address this question in the setting in w...

Split Bregman Method for Sparse Inverse Covariance Estimation with Matrix Iteration Acceleration

We consider the problem of estimating the inverse covariance matrix by maximizing the likelihood function with a penalty added to encourage the sparsity of the resulting matrix. We propose a new approach based on the split Bregman method to solve the regularized maximum likelihood estimation problem. We show that our method is significantly faster than the widely used graphical lasso method, wh...