Release time:2020-03-23  Hits:

• Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
• Journal:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
• Key Words:Conditional Gaussian graphical model (CGGM) multivariate linear regression sparse group Lasso sparsistency structured output
• Abstract:In this paper, we propose a joint conditional graphical Lasso to learn multiple conditional Gaussian graphical models, also known as Gaussian conditional random fields, with some similar structures. Our model builds on the maximum likelihood method with the convex sparse group Lasso penalty. Moreover, our model is able to model multiple multivariate linear regressions with unknown noise covariances via a convex formulation. In addition, we develop an efficient approximated Newton's method for optimizing our model. Theoretically, we establish the asymptotic properties of our model on consistency and sparsistency under the high-dimensional settings. Finally, extensive numerical results on simulations and real data sets demonstrate that our method outperforms the compared methods on structure recovery and structured output prediction. To the best of our knowledge, the joint learning of multiple multivariate regressions with unknown covariance is first studied.
• ISSN No.:2162-237X
• Translation or Not:no
• Co-author:csc,Sheng Jun Huang
• Correspondence Author:huangfeihu
• Date of Publication:2018-07-01