Online Parameter Estimation Over Distributed Multitask Networks With A Rank-one Model

Yitong Chen, Danqi Jin, Jie Chen, Cédric Richard, Wen Zhang, Gongping Huang, Jingdong Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

In recent years, modeling multitask relations in distributed networks has garnered considerable attention. Motivated by various practical applications, we propose a novel distributed multitask network model, termed the rank-one model, where each optimum vector to be estimated is a scaled representation of the others. To address the optimization problem with the rank-one constraint in a distributed manner, it is crucial to decouple the variables within the constraint, achieved by locally relaxing it at each node. Subsequently, local constrained distributed optimization problems are resolved using the projected gradient descent method, with the added challenge of projecting onto a non-convex rank-one space. Efficient evaluation of this projection is achieved using the computationally efficient power method. Additionally, theoretical analyses are performed on the proposed algorithm, particularly focusing on a special case of star topologies, with provided conditions ensuring stability in both the mean and mean-square senses. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

源语言英语
主期刊名32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
出版商European Signal Processing Conference, EUSIPCO
1042-1046
页数5
ISBN(电子版)9789464593617
DOI
出版状态已出版 - 2024
活动32nd European Signal Processing Conference, EUSIPCO 2024 - Lyon, 法国
期限: 26 8月 202430 8月 2024

出版系列

姓名European Signal Processing Conference
ISSN(印刷版)2219-5491

会议

会议32nd European Signal Processing Conference, EUSIPCO 2024
国家/地区法国
Lyon
时期26/08/2430/08/24

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