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arXiv · 1703.01888

A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation

Yuan Wang·Wee Peng Tay·Wuhua Hu
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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.

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Yuan Wang, Wee Peng Tay, Wuhua Hu. 2017-03-03. A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation. https://doi.org/10.1109/jstsp.2017.2679339

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