title: The joint graphical lasso for inverse covariance estimation across multiple classes.
abstract: We consider the problem of estimating multiple related Gaussian graphical models from a high-dimensional data set with observations belonging to distinct classes. We propose thejoint graphical lasso, which borrows strength across the classes in order to estimate multiple graphical models that share certain characteristics, such as the locations or weights of nonzero edges. Our approach is based upon maximizing a penalized log likelihood. We employ generalized fused lasso or group lasso penalties, and implement a fast ADMM algorithm to solve the corresponding convex optimization problems. The performance of the proposed method is illustrated through simulated and real data examples.
authors.affiliation: Department of Biostatistics, University of Washington, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Danaher
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, USA.
authors.forename: Pei
authors.initials: P
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, University of Washington, USA.
authors.forename: Daniela M
authors.initials: DM
authors.lastname: Witten
keywords: Gaussian graphical model; alternating directions method of multipliers; generalized fused lasso; graphical lasso; group lasso; high-dimensional; network estimation