UM
Regularized robust Broad Learning System for uncertain data modeling
Jin, Jun-Wei1; Chen, C. L. Philip1,2
2018-12-17
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume322Pages:58-69
AbstractBroad Learning System (BLS) has achieved outstanding performance in classification and regression problems. Specifically, the accuracy and efficiency can be balanced well by BLS. However, the presence of outliers in data may destroy the stability and generality of standard BLS. In this paper, we propose the robust version of BLS (RBLS) to treat the data modeling with outliers. By assuming the regression residual and output weights follow their respective distributions, the objective function for RBLS is derived and the output weights for robust modeling can be determined by maximum a posterior estimation. Then the robustness of RBLS can be enhanced further by integrating the regularization theory. The Augmented Lagrange Multiplier method is utilized to optimize the novel models efficiently, and a solid theoretical proof is given to guarantee that the proposed RBLS is more robust than the standard BLS. Extensive experiments on function approximation and real-world regression are carried out to demonstrate that our proposed RBLS model can achieve a better modeling performance in uncertain data environment than the standard BLS and other regression algorithms. (C) 2018 Elsevier B.V. All rights reserved.
KeywordBroad Learning System Outliers Robustness Augmented Lagrange Multiplier Regularization Laplacian distribution
DOI10.1016/j.neucom.2018.09.028
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000447624800006
PublisherELSEVIER SCIENCE BV
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Univ Macau, Fac Sci & Technol, Taipa 99999, Macao, Peoples R China;
2.Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jin, Jun-Wei,Chen, C. L. Philip. Regularized robust Broad Learning System for uncertain data modeling[J]. NEUROCOMPUTING,2018,322:58-69.
APA Jin, Jun-Wei,&Chen, C. L. Philip.(2018).Regularized robust Broad Learning System for uncertain data modeling.NEUROCOMPUTING,322,58-69.
MLA Jin, Jun-Wei,et al."Regularized robust Broad Learning System for uncertain data modeling".NEUROCOMPUTING 322(2018):58-69.
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