A multitask diffusion strategy with optimized inter-cluster cooperation
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 diffusio...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2019
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/89364 http://hdl.handle.net/10220/47840 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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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