Broad Learning System Based on Maximum Correntropy Criterion

doi:10.1109/TNNLS.2020.3009417

	Broad Learning System Based on Maximum Correntropy Criterion
	Zheng, Yunfei 1; Chen, Badong 1; Wang, Shiyuan 2; Wang, Weiqun3
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021-07-01
卷号	32 期号:7 页码:3083-3097
通讯作者	Chen, Badong(chenbd@mail.xjtu.edu.cn)
摘要	As an effective and efficient discriminative learning method, broad learning system (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems. However, the standard BLS is derived under the minimum mean square error (MMSE) criterion, which is, of course, not always a good choice due to its sensitivity to outliers. To enhance the robustness of BLS, we propose in this work to adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a correntropy-based BLS (C-BLS). Due to the inherent superiorities of MCC, the proposed C-BLS is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in the Gaussian or noise-free environment. In addition, three alternative incremental learning algorithms, derived from a weighted regularized least-squares solution rather than pseudoinverse formula, for C-BLS are developed. With the incremental learning algorithms, the system can be updated quickly without the entire retraining process from the beginning when some new samples arrive or the network deems to be expanded. Experiments on various regression and classification data sets are reported to demonstrate the desirable performance of the new methods.
关键词	Learning systems Robustness Standards Optimization Training Perturbation methods Mean square error methods Broad learning system (BLS) incremental learning algorithms maximum correntropy criterion (MCC) regression and classification
DOI	10.1109/TNNLS.2020.3009417
关键词[WOS]	FIXED-POINT ALGORITHM ; UNIVERSAL APPROXIMATION ; NEURAL-NETWORKS ; CONVERGENCE
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:000670541500023
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：45[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/173211/45253
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, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zheng, Yunfei,Chen, Badong,Wang, Shiyuan,et al. Broad Learning System Based on Maximum Correntropy Criterion[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(7):3083-3097.
APA	Zheng, Yunfei,Chen, Badong,Wang, Shiyuan,&Wang, Weiqun.(2021).Broad Learning System Based on Maximum Correntropy Criterion.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(7),3083-3097.
MLA	Zheng, Yunfei,et al."Broad Learning System Based on Maximum Correntropy Criterion".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.7(2021):3083-3097.