Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks
This paper focuses on the problem of distributed adaptive estimation over dynamic multi-task networks, where a set of nodes is required to collectively estimate some parameters of interest from noisy measurements. Besides, since nodes in the network are constrained by communication power consumption...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8959208/ |
| _version_ | 1818664379687632896 |
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| author | Qing Shi Fuliang He Jiagui Wu Feng Chen |
| author_facet | Qing Shi Fuliang He Jiagui Wu Feng Chen |
| author_sort | Qing Shi |
| collection | DOAJ |
| description | This paper focuses on the problem of distributed adaptive estimation over dynamic multi-task networks, where a set of nodes is required to collectively estimate some parameters of interest from noisy measurements. Besides, since nodes in the network are constrained by communication power consumption and external interference in a non-stationary environment, the objective pursued by the node is prone to change or abnormality. The problem is worth considering in several contexts including multi-target tracking, multi-model classification and heterogeneous network segmentation. We propose a distributed adaptive clustering strategy, which is mainly composed of two procedures: normal task adaptation and the same task cluster. The task anomaly detection based on non-cooperative least-mean-squares (NC-LMS) algorithm and task switching detection based on diffusion maximum correntropy criterion (D-MCC) algorithm are provided. A series of scenarios, such as dynamic network, time-varying tasks and non-stationary (Gaussian and pulse interference) are simulated. We also discuss optimization schemes to design the NC-LMS and D-MCC weights and examine the estimate performance and clustering effects of the proposed algorithm by simulation results. |
| first_indexed | 2024-12-17T05:31:49Z |
| format | Article |
| id | doaj.art-e9c501c33c6145ed9de9650ef77da096 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:31:49Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e9c501c33c6145ed9de9650ef77da0962022-12-21T22:01:43ZengIEEEIEEE Access2169-35362020-01-018124021241210.1109/ACCESS.2020.29665088959208Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task NetworksQing Shi0Fuliang He1https://orcid.org/0000-0003-4495-5063Jiagui Wu2https://orcid.org/0000-0003-2743-5162Feng Chen3https://orcid.org/0000-0002-9054-6570College of Artificial Intelligence, Southwest University, Chongqing, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing, ChinaCollege of Artificial Intelligence, Southwest University, Chongqing, ChinaThis paper focuses on the problem of distributed adaptive estimation over dynamic multi-task networks, where a set of nodes is required to collectively estimate some parameters of interest from noisy measurements. Besides, since nodes in the network are constrained by communication power consumption and external interference in a non-stationary environment, the objective pursued by the node is prone to change or abnormality. The problem is worth considering in several contexts including multi-target tracking, multi-model classification and heterogeneous network segmentation. We propose a distributed adaptive clustering strategy, which is mainly composed of two procedures: normal task adaptation and the same task cluster. The task anomaly detection based on non-cooperative least-mean-squares (NC-LMS) algorithm and task switching detection based on diffusion maximum correntropy criterion (D-MCC) algorithm are provided. A series of scenarios, such as dynamic network, time-varying tasks and non-stationary (Gaussian and pulse interference) are simulated. We also discuss optimization schemes to design the NC-LMS and D-MCC weights and examine the estimate performance and clustering effects of the proposed algorithm by simulation results.https://ieeexplore.ieee.org/document/8959208/Adaptive clusteringdistributed estimationmulti-taskmaximum correntropy criterion |
| spellingShingle | Qing Shi Fuliang He Jiagui Wu Feng Chen Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks IEEE Access Adaptive clustering distributed estimation multi-task maximum correntropy criterion |
| title | Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks |
| title_full | Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks |
| title_fullStr | Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks |
| title_full_unstemmed | Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks |
| title_short | Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks |
| title_sort | distributed adaptive clustering based on maximum correntropy criterion over dynamic multi task networks |
| topic | Adaptive clustering distributed estimation multi-task maximum correntropy criterion |
| url | https://ieeexplore.ieee.org/document/8959208/ |
| work_keys_str_mv | AT qingshi distributedadaptiveclusteringbasedonmaximumcorrentropycriterionoverdynamicmultitasknetworks AT fulianghe distributedadaptiveclusteringbasedonmaximumcorrentropycriterionoverdynamicmultitasknetworks AT jiaguiwu distributedadaptiveclusteringbasedonmaximumcorrentropycriterionoverdynamicmultitasknetworks AT fengchen distributedadaptiveclusteringbasedonmaximumcorrentropycriterionoverdynamicmultitasknetworks |