Covariance Selection over Networks
2025
会议录名称PROCEEDINGS OF THE 28TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS
ISSN2640-3498
发表状态正式接收
摘要

Covariance matrix estimation is a fundamental problem in multivariate data analysis, which becomes particularly challenging in high-dimensional settings due to the curse of dimensionality. To enhance estimation accuracy, structural regularization is often imposed on the precision matrix (the inverse covariance matrix) for covariance selection. In this paper, we study covariance selection in a distributed setting, where data is spread across a network of agents. We formulate the problem as a Gaussian maximum likelihood estimation problem with structural penalties and propose a novel algorithmic framework called NetGGM. Unlike existing methods that rely on a central coordinator, NetGGM operates in a fully decentralized manner with low computational complexity. We provide theoretical guarantees showing that NetGGM converges linearly to the global optimum while ensuring consensus among agents. Numerical experiments validate its convergence properties and demonstrate that it outperforms state-of-the-art methods in precision matrix estimation.

会议录编者/会议主办者Society for Artificial Intelligence and Statistics
关键词Covariance matrix estimation distributed optimization gradient tracking
会议名称The 28th International Conference on Artificial Intelligence and Statistics
会议地点Splash Beach Resort, Mai Khao THAILAND
会议日期May 3-5, 2025
学科门类工学::计算机科学与技术(可授工学、理学学位)
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收录类别EI
语种英语
出版者Proceedings of Machine Learning Research
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/510716
专题信息科学与技术学院_博士生
信息科学与技术学院_硕士生
信息科学与技术学院_PI研究组_赵子平组
共同第一作者Li FP(栗丰沛)
通讯作者Zhao ZP(赵子平)
作者单位
1.上海科技大学信息学院
2.宾夕法尼亚州立大学电气工程与计算机科学学院
第一作者单位上海科技大学
通讯作者单位上海科技大学
第一作者的第一单位上海科技大学
推荐引用方式
GB/T 7714
Xia WF,Li FP,Sun Y,et al. Covariance Selection over Networks[C]//Society for Artificial Intelligence and Statistics:Proceedings of Machine Learning Research,2025.
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