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 the joint 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.
REF used ADMM to estimate multiple precision matrices with pairwise fused lasso penalty and group lasso penalty.
The joint graphical lasso for inverse covariance estimation across multiple classes
Journal of the Royal Statistical Society. Series B, Statistical methodology
Journal of the Royal Statistical Society. Series B, Statistical methodology
Mathematics Medicine