The joint graphical lasso for inverse covariance estimation across multiple classes
Patrick Danaher, Pei Wang, Daniela M. Witten
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2014 · Volume 76 · Issue 2
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 to estimate multiple graphical models that share certain characteristics, such as the locations or weights of non-zero edges. Our approach is based on maximizing a penalized log-likelihood. We employ generalized fused lasso or group lasso penalties and implement a fast alternating directions method of multipliers algorithm to solve the corresponding convex optimization problems. The performance of the method proposed is illustrated through simulated and real data examples.
Keywords
Alternating directions method of multipliers, Gaussian graphical model, Generalized fused lasso, Graphical lasso, Group lasso, High dimensional data, Network estimation
Enable JavaScript for citation tools and additional paper interactions.