Within- cross- consensus-view representation-based multi-view multi-label learning with incomplete data

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dc.authoridWang, Ling/0000-0003-0272-2974
dc.authoridMa, Wei/0000-0002-7344-998X
dc.authoridWang, Ling/0000-0003-0272-2974
dc.authoridwang, haoyu/0009-0001-2467-5331
dc.authoridZhu, Changming/0000-0001-8039-5601
dc.authorwosidWang, Ling/AGR-4917-2022
dc.authorwosidMa, Wei/JXY-5019-2024
dc.authorwosidWang, Siying/KHX-1894-2024
dc.authorwosidWang, Yifan/KDO-8319-2024
dc.authorwosidWang, Ling/KBA-9814-2024
dc.authorwosidyan, su/KHT-1728-2024
dc.authorwosidzou, yao/KCK-8222-2024
dc.contributor.authorZhu, Changming
dc.contributor.authorLiu, Yanchen
dc.contributor.authorMiao, Duoqian
dc.contributor.authorDong, Yilin
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:50:23Z
dc.date.available2024-05-19T14:50:23Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis article develops a multi-view multi-label learning for incomplete data which are ubiquitous with the usage of three kinds of representations including within-view representation, cross-view representation, and consensus-view representation. Different from the recent learning machines, the proposed learning machine takes the feature-oriented information, label-oriented information, and associated information between features and labels in multiple representations together and exploits the hidden useful information of available instances with the usage of instance-instance correlations, feature-feature correlations, label-label correlations, and feature-label correlations. The developed learning machine is named as within- cross-consensus view representation-based multi-view multi-label learning with incomplete data (WCC-MVML-ID). Extensive experiments on multiple multi-view and multi-label data sets with incomplete data validate the effectiveness of WCC-MVML-ID and it can be concluded that (1) WCC-MVML-ID outperforms other compared learning machines and its performances are more stable even though the missing rates of features and labels being larger; (2) compared with within-view information and consensus-view information, cross-view information is more useful for the processing problem about incomplete data; (3) WCC-MVML-ID can converge within 45 iterations.en_US
dc.description.sponsorshipNational Natural Science Foundation of China (CN) [62276164, 61602296, 62076182]; 'Science and technology innovation action plan' Natural Science Foundation of Shanghai, China [22ZR1427000]; Chenguang Program - Shanghai Education Development Foundation, China; Shanghai Municipal Education Commission, China [18CG54]en_US
dc.description.sponsorshipThis work is supported by National Natural Science Foundation of China (CN) [grant numbers 62276164, 61602296, and 62076182],'Science and technology innovation action plan' Natural Science Foundation of Shanghai, China [grant number 22ZR1427000]. Furthermore, this work is also sponsored by 'Chenguang Program' supported by Shanghai Education Development Foundation, China and Shanghai Municipal Education Commission, China [grant number 18CG54]. The authors would like to thank their support.en_US
dc.identifier.doi10.1016/j.neucom.2023.126729
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.scopus2-s2.0-85171627339en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.neucom.2023.126729
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5689
dc.identifier.volume557en_US
dc.identifier.wosWOS:001076388300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofNeurocomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectWithin-View Representationen_US
dc.subjectCross-View Representationen_US
dc.subjectConsensus-View Representationen_US
dc.subjectMulti-View Multi-Labelen_US
dc.subjectIncomplete Dataen_US
dc.titleWithin- cross- consensus-view representation-based multi-view multi-label learning with incomplete dataen_US
dc.typeArticleen_US

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