Mixture Correntropy-Based Kernel Extreme Learning Machines

doi:10.1109/TNNLS.2020.3029198

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Mixture Correntropy-Based Kernel Extreme Learning Machines
	Zheng, Yunfei 1; Chen, Badong 1; Wang, Shiyuan 4; Wang, Weiqun 3; Qin, Wei 2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2022-02-01
卷号	33 期号:2 页码:811-825
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
ISSN号	2162-237X
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.
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关键词	FIXED-POINT ALGORITHM ; UNIVERSAL APPROXIMATION ; CONVERGENCE ; REGRESSION ; NETWORKS ; EEG
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
内容类型	期刊论文
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.