Mixture Correntropy-Based Kernel Extreme Learning Machines

doi:10.1109/TNNLS.2020.3029198

	Mixture Correntropy-Based Kernel Extreme Learning Machines
	Zheng, Yunfei 1; Chen, Badong 1; Wang, Shiyuan 2; Wang, Weiqun3 ; Qin, Wei 4
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2022-02-01
卷号	33 期号:2 页码:811-825
通讯作者	Chen, Badong(chenbd@mail.xjtu.edu.cn)
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.
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
关键词[WOS]	FIXED-POINT ALGORITHM ; UNIVERSAL APPROXIMATION ; CONVERGENCE ; REGRESSION ; NETWORKS ; EEG
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000752016400031
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47347
专题	复杂系统认知与决策实验室_先进机器人
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.Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China 3.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 4.Xidian Univ, Sch Life Sci & Technol, Xian 710071, 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.