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Ensemble Extreme Learning Machine and Sparse Representation Classification
Cao, J.; Hao, J.; Lai, X.; Vong, C. M.; Luo, M.
2016-11-01
Source PublicationJournal of the Franklin Institute (SCI-E)
ISSN0016-0032
Pages4526-4541
AbstractExtreme learning machine (ELM) combining with sparse representation classification (ELM-SRC) has been developed for image classification recently.However,employing a single ELM network with random hidden parameters may lead to unstable generalization and data partition performance in ELM-SRC.To alleviate this deficiency, we propose an enhanced ensemble based ELM and SRC algorithm (En-SRC) in this paper. Rather than using the output of a single ELM to decide the threshold for data partition, En-SRC incorporates multiple ensembles to enhance the reliability of the classifier. Different from ELM-SRC, a theoretical analysis on the data partition threshold selection of En-SRC is given. Extension to the ensemble based regularized ELM with SRC (EnR-SRC) is also presented in the paper.Experiments on a number of benchmark classification databases show that the proposed methods win a better classification performance with a lower computational complexity than the ELM-SRC approach.
KeywordEnsemble Extreme Learning Machine Sparse Representation
Language英語English
The Source to ArticlePB_Publication
PUB ID28775
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorCao, J.
Recommended Citation
GB/T 7714
Cao, J.,Hao, J.,Lai, X.,et al. Ensemble Extreme Learning Machine and Sparse Representation Classification[J]. Journal of the Franklin Institute (SCI-E), 2016, 4526-4541.
APA Cao, J.., Hao, J.., Lai, X.., Vong, C. M.., & Luo, M. (2016). Ensemble Extreme Learning Machine and Sparse Representation Classification. Journal of the Franklin Institute (SCI-E), 4526-4541.
MLA Cao, J.,et al."Ensemble Extreme Learning Machine and Sparse Representation Classification".Journal of the Franklin Institute (SCI-E) (2016):4526-4541.
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