Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161793
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHu, Minghuien_US
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnamen_US
dc.date.accessioned2022-09-20T05:40:41Z-
dc.date.available2022-09-20T05:40:41Z-
dc.date.issued2022-
dc.identifier.citationHu, M. & Suganthan, P. N. (2022). Representation learning using deep random vector functional link networks for clustering. Pattern Recognition, 129, 108744-. https://dx.doi.org/10.1016/j.patcog.2022.108744en_US
dc.identifier.issn0031-3203en_US
dc.identifier.urihttps://hdl.handle.net/10356/161793-
dc.description.abstractRandom Vector Functional Link (RVFL) Networks have received a lot of attention due to the fast training speed as the non-iterative solution characteristic. Currently, the main research direction of RVFLs has supervised learning, including semi-supervised and multi-label. There are hardly any unsupervised research results for RVFLs. In this paper, we propose the unsupervised RVFL (usRVFL), and the unsupervised framework is generic that can be used with other RVFL variants, thus we extend it to an ensemble deep variant, unsupervised deep RVFL (usdRVFL). The unsupervised method is based on the manifold regularization while the deep variant is related to the consensus clustering method, which can increase the capability and diversity of RVFLs. Our unsupervised approaches also benefit from fast training speed, even the deep variant offers a very competitive computation efficiency. Empirical experiments on several benchmark datasets demonstrated the effectiveness of the proposed algorithms.en_US
dc.language.isoenen_US
dc.relation.ispartofPattern Recognitionen_US
dc.rights© 2022 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleRepresentation learning using deep random vector functional link networks for clusteringen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.patcog.2022.108744-
dc.identifier.scopus2-s2.0-85129276700-
dc.identifier.volume129en_US
dc.identifier.spage108744en_US
dc.subject.keywordsRandom Vector Functional Linken_US
dc.subject.keywordsUnsupervised Learningen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:EEE Journal Articles

SCOPUSTM   
Citations 20

16
Updated on Apr 11, 2024

Web of ScienceTM
Citations 20

14
Updated on Oct 31, 2023

Page view(s)

98
Updated on Apr 15, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.