Mixture Correntropy-Based Kernel Extreme Learning Machines
文献类型：期刊论文
| 作者 | Zheng, Yunfei1; Chen, Badong1; Wang, Shiyuan4; Wang, Weiqun3 ; Qin, Wei2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2022-02-01 |
| 卷号 | 33期号:2页码:811-825 |
| ISSN号 | 2162-237X |
| 关键词 | Kernel Optimization Learning systems Robustness Support vector machines Mean square error methods Extreme learning machine (ELM) kernel method mixture correntropy online learning |
| DOI | 10.1109/TNNLS.2020.3029198 |
| 通讯作者 | Chen, Badong(chenbd@mail.xjtu.edu.cn) |
| 英文摘要 | Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel learning, has achieved outstanding performance in addressing various regression and classification problems. Compared with the basic ELM, KELM has a better generalization ability owing to no needs of the number of hidden nodes given beforehand and random projection mechanism. Since KELM is derived under the minimum mean square error (MMSE) criterion for the Gaussian assumption of noise, its performance may deteriorate under the non-Gaussian cases, seriously. To improve the robustness of KELM, this article proposes a mixture correntropy-based KELM (MC-KELM), which adopts the recently proposed maximum mixture correntropy criterion as the optimization criterion, instead of using the MMSE criterion. In addition, an online sequential version of MC-KELM (MCOS-KELM) is developed to deal with the case that the data arrive sequentially (one-by-one or chunk-by-chunk). Experimental results on regression and classification data sets are reported to validate the performance superiorities of the new methods. |
| WOS关键词 | FIXED-POINT ALGORITHM ; UNIVERSAL APPROXIMATION ; CONVERGENCE ; REGRESSION ; NETWORKS ; EEG |
| 资助项目 | National Natural Science Foundation of China[91648208] ; National Natural Science Foundation of China[61976175] ; National Natural Science Foundation-Shenzhen Joint Research Program[U1613219] ; Key Project of Natural Science Basic Research Plan in Shaanxi Province of China[2019JZ-05] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000752016400031 |
| 资助机构 | National Natural Science Foundation of China ; National Natural Science Foundation-Shenzhen Joint Research Program ; Key Project of Natural Science Basic Research Plan in Shaanxi Province of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47346]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Chen, Badong |
| 作者单位 | 1.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China 2.Xidian Univ, Sch Life Sci & Technol, Xian 710071, Peoples R China 3.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 4.Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Yunfei,Chen, Badong,Wang, Shiyuan,et al. Mixture Correntropy-Based Kernel Extreme Learning Machines[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(2):811-825. |
| APA | Zheng, Yunfei,Chen, Badong,Wang, Shiyuan,Wang, Weiqun,&Qin, Wei.(2022).Mixture Correntropy-Based Kernel Extreme Learning Machines.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(2),811-825. |
| MLA | Zheng, Yunfei,et al."Mixture Correntropy-Based Kernel Extreme Learning Machines".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.2(2022):811-825. |
入库方式： OAI收割
来源：自动化研究所
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