The joint graphical lasso for inverse covariance estimation across multiple classes

• 成果类型: Article
• 署名作者: Danaher, Patrick; Wang, Pei; Witten, Daniela M.
• 署名单位: University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12033
• 发表日期: 2014
• 页码: 373-397
• 关键词: regression; selection; expression; MODEL

摘要

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 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.