The cluster graphical lasso for improved estimation of Gaussian graphical models.

ABSTRACT: The task of estimating a Gaussian graphical model in the high-dimensional setting is considered. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to a lasso penalty, is a well-studied approach for this task. A surprising connection between the graphical lasso and hierarchical clustering is introduced: the graphical lasso in effect performs a two-step procedure, in which (1) single linkage hierarchical clustering is performed on the variables in order to identify connected components, and then (2) a penalized log likelihood is maximized on the subset of variables within each connected component. Thus, the graphical lasso determines the connected components of the estimated network via single linkage clustering. The single linkage clustering is known

SUBMITTER: Tan KM 

PROVIDER: S-EPMC4307846 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature