A Multitask Diffusion Strategy With Optimized Inter-cluster Cooperation
Author
Wang, Yuan
Tay, Wee Peng
Hu, Wuhua
Date of Issue
2017School
School of Electrical and Electronic Engineering
Version
Accepted version
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.
Subject
Distributed estimation
Diffusion strategy
Diffusion strategy
Type
Journal Article
Series/Journal Title
IEEE Journal of Selected Topics in Signal Processing
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].
Collections
http://dx.doi.org/10.1109/JSTSP.2017.2679339
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