Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81466
Title: A Multitask Diffusion Strategy With Optimized Inter-cluster Cooperation
Authors: Wang, Yuan
Tay, Wee Peng
Hu, Wuhua
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Diffusion strategy
Distributed estimation
Issue Date: 2017
Source: Wang, Y., Tay, W. P., & Hu, W. (2017). A Multitask Diffusion Strategy With Optimized Inter-cluster Cooperation. IEEE Journal of Selected Topics in Signal Processing, 11(3), 504-517.
Series/Report no.: IEEE Journal of Selected Topics in Signal Processing
Abstract: We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.
URI: https://hdl.handle.net/10356/81466
http://hdl.handle.net/10220/43469
ISSN: 1932-4553
DOI: http://dx.doi.org/10.1109/JSTSP.2017.2679339
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/JSTSP.2017.2679339].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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