期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 65, 期 13, 页码 3526-3537出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2017.2692736
关键词
Decentralized clustering; cooperative processing; multi-task networks; belief update; diffusion strategy; adaptive networks
资金
- NSF [ECCS-1407712, CCF-1524250]
- Direct For Computer & Info Scie & Enginr [1524250] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations [1524250] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1407712] Funding Source: National Science Foundation
We consider the problem of decentralized clustering and estimation over multitask networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance. One key feature of the proposed technique is the integration of the learning and clustering tasks into a single strategy. We analyze the performance of the procedure and show that the error probabilities of types I and II decay exponentially to zero with the step-size parameter. While links between agents following different objectives are ignored in the clustering process, we nevertheless show how to exploit these links to relay critical information across the network for enhanced performance. Simulation results illustrate the performance of the proposed method in comparison to other useful techniques.
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