{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T05:07:43Z","timestamp":1733634463507,"version":"3.30.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"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":["Pattern Recognition"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.patcog.2024.111195","type":"journal-article","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T16:11:32Z","timestamp":1731773492000},"page":"111195","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Debiasing weighted multi-view k-means clustering based on causal regularization"],"prefix":"10.1016","volume":"160","author":[{"given":"Xiuqi","family":"Huang","sequence":"first","affiliation":[]},{"given":"Hong","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Haotian","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Chenping","family":"Hou","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.patcog.2024.111195_b1","doi-asserted-by":"crossref","first-page":"2168","DOI":"10.1109\/TPAMI.2020.3031898","article-title":"Small data challenges in big data era: A survey of recent progress on unsupervised and semi-supervised methods","volume":"44","author":"Qi","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2024.111195_b2","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1016\/j.neucom.2017.06.053","article-title":"A review of clustering techniques and developments","volume":"267","author":"Saxena","year":"2017","journal-title":"Neurocomputing"},{"issue":"94","key":"10.1016\/j.patcog.2024.111195_b3","article-title":"Feature selection: A data perspective","volume":"50","author":"Li","year":"2018","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"10.1016\/j.patcog.2024.111195_b4","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s10462-019-09682-y","article-title":"A review of unsupervised feature selection methods","volume":"53","author":"Solorio-Fernandez","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.patcog.2024.111195_b5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.knosys.2015.02.017","article-title":"Correlation and instance based feature selection for electricity load forecasting","volume":"82","author":"Koprinska","year":"2015","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"10.1016\/j.patcog.2024.111195_b6","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/TKDE.2018.2836440","article-title":"A correlation-based feature weighting filter for naive bayes","volume":"31","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.patcog.2024.111195_b7","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s42256-022-00445-z","article-title":"Stable learning establishes some common ground between causal inference and machine learning","volume":"4","author":"Cui","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.patcog.2024.111195_b8","doi-asserted-by":"crossref","unstructured":"Z. Shen, P. Cui, K. Kuang, B. Li, P. Chen, Causally regularized learning with agnostic data selection bias, in: Proceedings of the ACM International Conference on Multimedia, 2018, pp. 411\u2013419.","DOI":"10.1145\/3240508.3240577"},{"issue":"1","key":"10.1016\/j.patcog.2024.111195_b9","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1890\/07-2153.1","article-title":"Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data","volume":"19","author":"Phillips","year":"2009","journal-title":"Ecol. Appl."},{"key":"10.1016\/j.patcog.2024.111195_b10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: Recent progress and new challenges","volume":"38","author":"Zhao","year":"2017","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2024.111195_b11","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.inffus.2021.11.003","article-title":"Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications","volume":"81","author":"Chou","year":"2022","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2024.111195_b12","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","article-title":"Clustering-based undersampling in class-imbalanced data","volume":"409","author":"Lin","year":"2017","journal-title":"Inform. Sci."},{"key":"10.1016\/j.patcog.2024.111195_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108428","article-title":"Weighted clustering ensemble: A review","volume":"124","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"issue":"8","key":"10.1016\/j.patcog.2024.111195_b14","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1109\/TPAMI.2006.168","article-title":"On weighting clustering","volume":"28","author":"Nock","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"10.1016\/j.patcog.2024.111195_b15","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1016\/j.camwa.2011.07.005","article-title":"Sample-weighted clustering methods","volume":"62","author":"Yu","year":"2011","journal-title":"Comput. Math. Appl."},{"issue":"5","key":"10.1016\/j.patcog.2024.111195_b16","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","article-title":"Automated variable weighting in k-means type clustering","volume":"27","author":"Huang","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2024.111195_b17","unstructured":"X. Cai, F. Nie, H. Huang, Multi-view k-means clustering on big data, in: Proceedings of the International Joint Conference on Artificial Intelligence, 2013, pp. 2598\u20132604."},{"key":"10.1016\/j.patcog.2024.111195_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2023.104118","article-title":"Multi-view clustering for multiple manifold learning via concept factorization","volume":"140","author":"Khan","year":"2023","journal-title":"Digit. Signal Process."},{"issue":"19","key":"10.1016\/j.patcog.2024.111195_b19","doi-asserted-by":"crossref","first-page":"22511","DOI":"10.1007\/s10489-023-04716-z","article-title":"Multi-view subspace clustering for learning joint representation via low-rank sparse representation","volume":"53","author":"Khan","year":"2023","journal-title":"Appl. Intell."},{"key":"10.1016\/j.patcog.2024.111195_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109764","article-title":"Auto-attention mechanism for multi-view deep embedding clustering","volume":"143","author":"Diallo","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111195_b21","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s13042-021-01394-6","article-title":"Multi-view low rank sparse representation method for three-way clustering","volume":"13","author":"Khan","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"4","key":"10.1016\/j.patcog.2024.111195_b22","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","article-title":"A review on ensembles for the class imbalance problem: Bagging-boosting-, and hybrid-based approaches","volume":"42","author":"Galar","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C-Applications Rev."},{"key":"10.1016\/j.patcog.2024.111195_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110415","article-title":"A broad review on class imbalance learning techniques","volume":"143","author":"Rezvani","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.patcog.2024.111195_b24","series-title":"International Conference on Algorithmic Learning Theory","first-page":"38","article-title":"Sample selection bias correction theory","author":"Cortes","year":"2008"},{"key":"10.1016\/j.patcog.2024.111195_b25","doi-asserted-by":"crossref","unstructured":"B. Zadrozny, Learning and evaluating classifiers under sample selection bias, in: Proceedings of the International Conference on Machine Learning, 2004, p. 114.","DOI":"10.1145\/1015330.1015425"},{"key":"10.1016\/j.patcog.2024.111195_b26","doi-asserted-by":"crossref","first-page":"153","DOI":"10.2307\/1912352","article-title":"Sample selection bias as a specification error","author":"Heckman","year":"1979","journal-title":"Econometrica: J. Sconometric Soc."},{"issue":"1","key":"10.1016\/j.patcog.2024.111195_b27","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1146\/annurev.so.18.080192.001551","article-title":"Models for sample selection bias","volume":"18","author":"Winship","year":"1992","journal-title":"Annu. Rev. Sociol."},{"issue":"1","key":"10.1016\/j.patcog.2024.111195_b28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","article-title":"The central role of the propensity score in observational studies for causal effects","volume":"70","author":"Rosenbaum","year":"1983","journal-title":"Biometrika"},{"issue":"260","key":"10.1016\/j.patcog.2024.111195_b29","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1080\/01621459.1952.10483446","article-title":"A generalization of sampling without replacement from a finite universe","volume":"47","author":"Horvitz","year":"1952","journal-title":"J. Amer. Statist. Assoc."},{"issue":"448","key":"10.1016\/j.patcog.2024.111195_b30","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1080\/01621459.1999.10473862","article-title":"Adjusting for nonignorable drop-out using semiparametric nonresponse models","volume":"94","author":"Scharfstein","year":"1999","journal-title":"J. Amer. Statist. Assoc."},{"issue":"1","key":"10.1016\/j.patcog.2024.111195_b31","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1093\/pan\/mpr025","article-title":"Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies","volume":"20","author":"Hainmueller","year":"2012","journal-title":"Political Anal."},{"issue":"511","key":"10.1016\/j.patcog.2024.111195_b32","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1080\/01621459.2015.1023805","article-title":"Stable weights that balance covariates for estimation with incomplete outcome data","volume":"110","author":"Zubizarreta","year":"2015","journal-title":"J. Amer. Statist. Assoc."},{"issue":"4","key":"10.1016\/j.patcog.2024.111195_b33","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1111\/rssb.12268","article-title":"Approximate residual balancing: Debiased inference of average treatment effects in high dimensions","volume":"80","author":"Athey","year":"2018","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.patcog.2024.111195_b34","doi-asserted-by":"crossref","unstructured":"K. Kuang, P. Cui, B. Li, M. Jiang, S. Yang, Estimating treatment effect in the wild via differentiated confounder balancing, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 265\u2013274.","DOI":"10.1145\/3097983.3098032"},{"issue":"1","key":"10.1016\/j.patcog.2024.111195_b35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3365677","article-title":"Treatment effect estimation via differentiated confounder balancing and regression","volume":"14","author":"Kuang","year":"2020","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.patcog.2024.111195_b36","doi-asserted-by":"crossref","unstructured":"K. Kuang, P. Cui, S. Athey, R. Xiong, B. Li, Stable prediction across unknown environments, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 1617\u20131626.","DOI":"10.1145\/3219819.3220082"},{"key":"10.1016\/j.patcog.2024.111195_b37","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4485","article-title":"Stable prediction with model misspecification and agnostic distribution shift","volume":"vol. 34","author":"Kuang","year":"2020"},{"key":"10.1016\/j.patcog.2024.111195_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126406","article-title":"Decorrelated spectral regression: An unsupervised dimension reduction method under data selection bias","author":"Huang","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patcog.2024.111195_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110033","article-title":"Feature incremental learning with causality","volume":"146","author":"Ni","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111195_b40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TPAMI.2024.3502456","article-title":"Generalizing graph neural networks on out-of-distribution graphs","author":"Fan","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2024.111195_b41","unstructured":"F. Nie, H. Huang, X. Cai, C. Ding, Efficient and robust feature selection via joint \u21132, 1-norms minimization, in: Proceedings of the Advances in Neural Information Processing Systems, (23) 2010, pp. 1813\u20131821."},{"key":"10.1016\/j.patcog.2024.111195_b42","doi-asserted-by":"crossref","unstructured":"C. Ding, X. He, H.D. Simon, Nonnegative lagrangian relaxation of k-means and spectral clustering, in: Proceedings of the European Conference on Machine Learning, 2005, pp. 530\u2013538.","DOI":"10.1007\/11564096_51"},{"key":"10.1016\/j.patcog.2024.111195_b43","doi-asserted-by":"crossref","unstructured":"F. Nie, J. Li, X. Li, Self-weighted multiview clustering with multiple graphs, in: Proceedings of the International Joint Conference on Artificial Intelligence, 2017, pp. 2564\u20132570.","DOI":"10.24963\/ijcai.2017\/357"},{"issue":"3","key":"10.1016\/j.patcog.2024.111195_b44","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1109\/TIP.2017.2754939","article-title":"Auto-weighted multi-view learning for image clustering and semi-supervised classification","volume":"27","author":"Nie","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2024.111195_b45","unstructured":"H. Tao, C. Hou, J. Zhu, D. Yi, Multi-view clustering with adaptively learned graph, in: Proceedings of the Asian Conference on Machine Learning, 2017, pp. 113\u2013128."},{"issue":"2","key":"10.1016\/j.patcog.2024.111195_b46","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1109\/TKDE.2020.2986201","article-title":"Multi-view k-means clustering with adaptive sparse memberships and weight allocation","volume":"34","author":"Han","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.patcog.2024.111195_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120480","article-title":"Landmark-based k-factorization multi-view subspace clustering","volume":"667","author":"Fang","year":"2024","journal-title":"Inform. Sci."},{"key":"10.1016\/j.patcog.2024.111195_b48","doi-asserted-by":"crossref","unstructured":"X. Zhang, Y. He, R. Xu, H. Yu, Z. Shen, P. Cui, Nico++: Towards better benchmarking for domain generalization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2023, pp. 16036\u201316047.","DOI":"10.1109\/CVPR52729.2023.01539"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320324009464?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320324009464?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T09:00:39Z","timestamp":1733562039000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320324009464"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":48,"alternative-id":["S0031320324009464"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2024.111195","relation":{},"ISSN":["0031-3203"],"issn-type":[{"type":"print","value":"0031-3203"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Debiasing weighted multi-view k-means clustering based on causal regularization","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2024.111195","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111195"}}