{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T02:02:12Z","timestamp":1780020132357,"version":"3.53.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116096","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:09:58Z","timestamp":1777568998000},"page":"116096","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep multi-view clustering via structure preserving learning"],"prefix":"10.1016","volume":"346","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8397-7716","authenticated-orcid":false,"given":"Yixuan","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6659-5727","authenticated-orcid":false,"given":"Wenming","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2600-9615","authenticated-orcid":false,"given":"Xiaolin","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7065-2517","authenticated-orcid":false,"given":"Qian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5503-1336","authenticated-orcid":false,"given":"Zhendong","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9106-9500","authenticated-orcid":false,"given":"Hongwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116096_b1","first-page":"1","article-title":"Bearing fault diagnosis under variable working conditions base on contrastive domain adaptation method","volume":"71","author":"An","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.knosys.2026.116096_b2","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TPAMI.2019.2922640","article-title":"Rotationnet for joint object categorization and unsupervised pose estimation from multi-view images","volume":"43","author":"Kanezaki","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116096_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.107055","article-title":"MFC-ACL: Multi-view fusion clustering with attentive contrastive learning","volume":"184","author":"Huang","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116096_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127118","article-title":"Multi-view subspace clustering via slack consistency and double-side orthogonal diversity","volume":"277","author":"Luo","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116096_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.105482","article-title":"Graph-regularized least squares regression for multi-view subspace clustering","volume":"194","author":"Chen","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116096_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124103","article-title":"Comprehensive multi-view self-representations for clustering","volume":"251","author":"Cheng","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116096_b7","first-page":"5924","article-title":"CGD: Multi-view clustering via cross-view graph diffusion","volume":"vol. 34","author":"Tang","year":"2020"},{"issue":"1","key":"10.1016\/j.knosys.2026.116096_b8","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/TPAMI.2020.3011148","article-title":"Multiview clustering: A scalable and parameter-free bipartite graph fusion method","volume":"44","author":"Li","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116096_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111020","article-title":"Exclusivity and consistency induced NMF for multi-view representation learning","volume":"281","author":"Huang","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116096_b10","article-title":"Mokan: A multi-omics data analysis framework using kolmogorov-arnold networks","author":"He","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.knosys.2026.116096_b11","doi-asserted-by":"crossref","unstructured":"S. Du, C. Wu, Z. Fang, W. Zhao, Y. Wu, C. Wang, S. Wang, Largemvc-net: anchor-based deep unfolding network for large-scale multi-view clustering, in: Proceedings of the 33rd ACM International Conference on Multimedia, 2025, pp. 1714\u20131723.","DOI":"10.1145\/3746027.3755396"},{"issue":"12","key":"10.1016\/j.knosys.2026.116096_b12","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s10462-024-10990-1","article-title":"Graph neural networks for multi-view learning: a taxonomic review","volume":"57","author":"Xiao","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.knosys.2026.116096_b13","doi-asserted-by":"crossref","first-page":"9203","DOI":"10.1109\/TMM.2023.3248173","article-title":"Dual fusion-propagation graph neural network for multi-view clustering","volume":"25","author":"Xiao","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116096_b14","article-title":"UNAGI: Unified neighbor-aware graph neural network for multi-view clustering","author":"Xu","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116096_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121298","article-title":"Deep multi-view graph clustering network with weighting mechanism and collaborative training","volume":"236","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.knosys.2026.116096_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3708887","article-title":"Semantic feature graph consistency with contrastive cluster assignments for multilingual document clustering","volume":"24","author":"Sun","year":"2025","journal-title":"ACM Trans. Asian Low-Resource Lang. Inf. Process."},{"key":"10.1016\/j.knosys.2026.116096_b17","doi-asserted-by":"crossref","DOI":"10.1109\/TMM.2024.3387298","article-title":"Self-weighted contrastive fusion for deep multi-view clustering","author":"Wu","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116096_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128264","article-title":"Multi-view clustering with semantic fusion and contrastive learning","volume":"603","author":"Yu","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116096_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109193","article-title":"A2AE: Towards adaptive multi-view graph representation learning via all-to-all graph autoencoder architecture","volume":"125","author":"Sun","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.knosys.2026.116096_b20","doi-asserted-by":"crossref","DOI":"10.1109\/TIP.2024.3444269","article-title":"Dual contrast-driven deep multi-view clustering","author":"Cui","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116096_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108196","article-title":"Consistent and diverse multi-view subspace clustering with structure constraint","volume":"121","author":"Si","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116096_b22","series-title":"Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11\u201314, 2021, Proceedings, Part II 26","first-page":"291","article-title":"Consistency-and inconsistency-aware multi-view subspace clustering","author":"Zhang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116096_b23","first-page":"20479","article-title":"Ambiguous instance-aware contrastive network with multi-level matching for multi-view document clustering","volume":"vol. 39","author":"Shu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116096_b24","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.neunet.2022.03.009","article-title":"Robust multi-view subspace clustering based on consensus representation and orthogonal diversity","volume":"150","author":"Zhao","year":"2022","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116096_b25","first-page":"7936","article-title":"Self-supervised graph attention networks for deep weighted multi-view clustering","volume":"vol. 37","author":"Huang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116096_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109972","article-title":"Robust online hashing with label semantic enhancement for cross-modal retrieval","volume":"145","author":"Li","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116096_b27","article-title":"Parameter-free deep multi-modal clustering with reliable contrastive learning","author":"Lou","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116096_b28","article-title":"Deep multiview clustering by pseudo-label guided contrastive learning and dual correlation learning","author":"Hu","year":"2024","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"12","key":"10.1016\/j.knosys.2026.116096_b29","doi-asserted-by":"crossref","first-page":"12350","DOI":"10.1109\/TKDE.2023.3270311","article-title":"A comprehensive survey on multi-view clustering","volume":"35","author":"Fang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"10.1016\/j.knosys.2026.116096_b30","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s41019-021-00159-z","article-title":"Deep multiple auto-encoder-based multi-view clustering","volume":"6","author":"Du","year":"2021","journal-title":"Data Sci. Eng."},{"key":"10.1016\/j.knosys.2026.116096_b31","doi-asserted-by":"crossref","unstructured":"S. Fan, X. Wang, C. Shi, E. Lu, K. Lin, B. Wang, One2multi graph autoencoder for multi-view graph clustering, in: Proceedings of the Web Conference 2020, 2020, pp. 3070\u20133076.","DOI":"10.1145\/3366423.3380079"},{"key":"10.1016\/j.knosys.2026.116096_b32","series-title":"Deep embedded multi-view clustering via jointly learning latent representations and graphs","author":"Huang","year":"2022"},{"key":"10.1016\/j.knosys.2026.116096_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124258","article-title":"View-interactive attention information alignment-guided fusion for incomplete multi-view clustering","volume":"252","author":"Shu","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.knosys.2026.116096_b34","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116096_b35","series-title":"2024 International Joint Conference on Neural Networks","first-page":"1","article-title":"Dual-adaptive fusion multi-view clustering based on graph autoencoder","author":"Niu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116096_b36","doi-asserted-by":"crossref","unstructured":"N. Mrabah, M. Bouguessa, R. Ksantini, Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering, in: IJCAI, 2022, pp. 3351\u20133357.","DOI":"10.24963\/ijcai.2022\/465"},{"key":"10.1016\/j.knosys.2026.116096_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108334","article-title":"Graph clustering via variational graph embedding","volume":"122","author":"Guo","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116096_b38","doi-asserted-by":"crossref","unstructured":"K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.knosys.2026.116096_b39","series-title":"International Conference on Machine Learning","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","author":"Xie","year":"2016"},{"key":"10.1016\/j.knosys.2026.116096_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106503","article-title":"Progressive neighbor-masked contrastive learning for fusion-style deep multi-view clustering","volume":"179","author":"Liu","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116096_b41","doi-asserted-by":"crossref","unstructured":"J. Xu, H. Tang, Y. Ren, L. Peng, X. Zhu, L. He, Multi-level feature learning for contrastive multi-view clustering, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16051\u201316060.","DOI":"10.1109\/CVPR52688.2022.01558"},{"issue":"4","key":"10.1016\/j.knosys.2026.116096_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.102967","article-title":"Contrastive and attentive graph learning for multi-view clustering","volume":"59","author":"Wang","year":"2022","journal-title":"Inf. Process. Manage."},{"key":"10.1016\/j.knosys.2026.116096_b43","doi-asserted-by":"crossref","unstructured":"Y. Lin, Y. Gou, Z. Liu, B. Li, J. Lv, X. Peng, Completer: Incomplete multi-view clustering via contrastive prediction, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 11174\u201311183.","DOI":"10.1109\/CVPR46437.2021.01102"},{"key":"10.1016\/j.knosys.2026.116096_b44","doi-asserted-by":"crossref","unstructured":"C. Kong, D. Lin, M. Bansal, R. Urtasun, S. Fidler, What are you talking about? text-to-image coreference, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3558\u20133565.","DOI":"10.1109\/CVPR.2014.455"},{"key":"10.1016\/j.knosys.2026.116096_b45","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"vol. 5","author":"MacQueen","year":"1967"},{"key":"10.1016\/j.knosys.2026.116096_b46","doi-asserted-by":"crossref","unstructured":"W. Yan, Y. Zhang, C. Lv, C. Tang, G. Yue, L. Liao, W. Lin, Gcfagg: Global and cross-view feature aggregation for multi-view clustering, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 19863\u201319872.","DOI":"10.1109\/CVPR52729.2023.01902"},{"key":"10.1016\/j.knosys.2026.116096_b47","doi-asserted-by":"crossref","unstructured":"X. Yang, J. Jiaqi, S. Wang, K. Liang, Y. Liu, Y. Wen, S. Liu, S. Zhou, X. Liu, E. Zhu, Dealmvc: Dual contrastive calibration for multi-view clustering, in: Proceedings of the 31st ACM International Conference on Multimedia, 2023, pp. 337\u2013346.","DOI":"10.1145\/3581783.3611951"},{"issue":"3","key":"10.1016\/j.knosys.2026.116096_b48","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10994-025-06735-y","article-title":"Deep contrastive coordinated multi-view consistency clustering","volume":"114","author":"Shi","year":"2025","journal-title":"Mach. Learn."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008221?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008221?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:07:29Z","timestamp":1780016849000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":48,"alternative-id":["S0950705126008221"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116096","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep multi-view clustering via structure preserving learning","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116096","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"116096"}}