The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
用于多类别逆协方差估计的联合图形套索
Summary 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 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…