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      A Multitask Diffusion Strategy With Optimized Inter-cluster Cooperation

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      A Multitask Diffusion Strategy With Optimized Inter-cluster Cooperation.pdf (1.543Mb)
      Author
      Wang, Yuan
      Tay, Wee Peng
      Hu, Wuhua
      Date of Issue
      2017
      School
      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
      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].
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      http://dx.doi.org/10.1109/JSTSP.2017.2679339
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