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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
关键字：Conditional Gaussian graphical model (CGGM) multivariate linear regression sparse group Lasso sparsistency structured output
摘要：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号：2162-237X
是否译文：否
发表时间：2018-07-01
合写作者：陈松灿,黄圣君
通讯作者：黄飞虎