Zhu, ChangmingLiu, YanchenMiao, DuoqianDong, YilinPedrycz, Witold2024-05-192024-05-1920230925-23121872-8286https://doi.org10.1016/j.neucom.2023.126729https://hdl.handle.net/20.500.12713/5689This 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.eninfo:eu-repo/semantics/closedAccessWithin-View RepresentationCross-View RepresentationConsensus-View RepresentationMulti-View Multi-LabelIncomplete DataArticle557WOS:0010763883000012-s2.0-85171627339N/A10.1016/j.neucom.2023.126729Q1