ShanghaiTech University Knowledge Management System
Covariance Selection over Networks | |
2025 | |
会议录名称 | PROCEEDINGS OF THE 28TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS |
ISSN | 2640-3498 |
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) |
URL | 查看原文 |
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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