{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T02:29:16Z","timestamp":1769048956742,"version":"3.49.0"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172263"],"award-info":[{"award-number":["62172263"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shandong, China","award":["ZR2020YQ47, ZR2019QF002"],"award-info":[{"award-number":["ZR2020YQ47, ZR2019QF002"]}]},{"name":"Youth Innovation Project of Shandong Universities, China","award":["2019KJN040"],"award-info":[{"award-number":["2019KJN040"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>Unsupervised multi-view feature selection aims to select informative features with multi-view features and unsupervised learning. It is a challenging problem due to the absence of explicit semantic supervision. Recently, graph theory and hard pseudo-label learning have been adopted to solve multi-view feature selection problems under the unsupervised learning paradigm. However, graph-based methods are difficult to support large-scale real scenarios due to the high computational complexity of graph construction. Moreover, existing methods based on hard pseudo-label learning generally result in significant information loss. In this article, we propose an Adaptive Collaborative Soft Label Learning (ACSLL) model for unsupervised multi-view feature selection. In this model, collaborative soft label learning and multi-view feature selection are integrated into a unified framework. Specifically, we learn the pseudo soft labels from each view feature by a simple and efficient method and fuse them with an adaptive weighting strategy into a joint soft label matrix. This matrix is further used for guiding the feature selection process to identify valuable features. An effective optimization strategy guaranteed with proven convergence is derived to iteratively solve this problem. Experiments demonstrate the superiority of the proposed method in both feature selection accuracy and efficiency.<\/jats:p>","DOI":"10.1145\/3591467","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T13:14:40Z","timestamp":1681132480000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Collaborative Soft Label Learning for Unsupervised Multi-View Feature Selection"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2773-9924","authenticated-orcid":false,"given":"Dan","family":"Shi","sequence":"first","affiliation":[{"name":"Shandong Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2993-7142","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shandong Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9519-612X","authenticated-orcid":false,"given":"Xiao","family":"Dong","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5274-4197","authenticated-orcid":false,"given":"Xuemeng","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5504-2529","authenticated-orcid":false,"given":"Jingjing","family":"Li","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1109-5028","authenticated-orcid":false,"given":"Zhiyong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"issue":"12","key":"e_1_3_2_2_2","first-page":"871","article-title":"The Laplacian spectrum of graphs","volume":"2","author":"Alavi Y.","year":"1991","unstructured":"Y. Alavi. 1991. The Laplacian spectrum of graphs. Graph Theory, Combinatorics, and Applications 2, 12 (1991), 871\u2013898.","journal-title":"Graph Theory, Combinatorics, and Applications"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.01.044"},{"issue":"3","key":"e_1_3_2_4_2","first-page":"937","article-title":"Collaborative multi-view K-means clustering","volume":"23","author":"Bettoumi Safa","year":"2019","unstructured":"Safa Bettoumi, Chiraz Jlassi, and Najet Arous. 2019. Collaborative multi-view K-means clustering. Soft Computing 23, 3 (2019), 937\u2013945.","journal-title":"Soft Computing"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33786-5"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/285"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2869476"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.5555\/1005332.1016787"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.35.11.652"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37331-2_26"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Gene H. Golub and Christian H. Reinsch. 2007. Singular value decomposition and least squares solutions. In Milestones in Matrix Computation - Selected Works of Gene H. Golub with Commentaries Oxford University Press 160\u2013180.","DOI":"10.1093\/oso\/9780199206810.003.0012"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.322"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.5555\/2976248.2976312"},{"key":"e_1_3_2_14_2","first-page":"3569","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Huang Jin","year":"2015","unstructured":"Jin Huang, Feiping Nie, and Heng Huang. 2015. A new simplex sparse learning model to measure data similarity for clustering. In Proceedings of the International Joint Conference on Artificial Intelligence. 3569\u20133575."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-1904-8"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.02.002"},{"key":"e_1_3_2_17_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Li Zechao","year":"2012","unstructured":"Zechao Li, Yi Yang, Jing Liu, Xiaofang Zhou, and Hanqing Lu. 2012. Unsupervised feature selection using nonnegative spectral analysis. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206845"},{"key":"e_1_3_2_19_2","first-page":"281","volume-title":"Proceedings of the International Conference on Berkeley Symposium on Mathematical Statistics and Probability","volume":"1","author":"MacQueen J. B.","year":"1967","unstructured":"J. B. MacQueen. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the International Conference on Berkeley Symposium on Mathematical Statistics and Probability. Vol. 1, 281\u2013297."},{"key":"e_1_3_2_20_2","article-title":"Experiments on high resolution images towards outdoor scene classification","author":"Monadjemi A.","year":"2002","unstructured":"A. Monadjemi, B. T. Thomas, and M. Mirmehdi. 2002. Experiments on high resolution images towards outdoor scene classification. In Proceedings of the Computer Vision Winter Workshop.","journal-title":"Proceedings of the Computer Vision Winter Workshop"},{"key":"e_1_3_2_21_2","first-page":"1813","volume-title":"Proceedings of the 23rd International Conference on Neural Information Processing Systems","author":"Nie Feiping","year":"2010","unstructured":"Feiping Nie, Heng Huang, Xiao Cai, and Chris H. Q. Ding. 2010. Efficient and robust feature selection via joint l2,1-norms minimization. In Proceedings of the 23rd International Conference on Neural Information Processing Systems. 1813\u20131821."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623726"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.5555\/3016100.3016174"},{"issue":"3","key":"e_1_3_2_24_2","first-page":"1210","article-title":"Structured graph optimization for unsupervised feature selection","volume":"33","author":"Nie Feiping","year":"2021","unstructured":"Feiping Nie, Wei Zhu, and Xuelong Li. 2021. Structured graph optimization for unsupervised feature selection. IEEE Transactions on Knowledge and Data Engineering 33, 3 (2021), 1210\u20131222.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944977"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2018.09.019"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2955209"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972832.30"},{"key":"e_1_3_2_29_2","first-page":"381","article-title":"Handwritten digit recognition by combined classifiers","volume":"34","author":"van Breukelen M.","year":"1998","unstructured":"M. van Breukelen, R. P. W. Duin, D. M. J. Tax, and J. E. den Hartog. 1998. Handwritten digit recognition by combined classifiers. Kybernetika 34, 4 (1998), 381\u2013386.","journal-title":"Kybernetika"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2983396"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2014.11.015"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2005.148"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3070646"},{"key":"e_1_3_2_34_2","first-page":"2224","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Xu Jinglin","year":"2016","unstructured":"Jinglin Xu, Junwei Han, Kai Xiong, and Feiping Nie. 2016. Robust and sparse fuzzy k-means clustering. In Proceedings of the International Joint Conference on Artificial Intelligence. 2224\u20132230."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273633"},{"issue":"2","key":"e_1_3_2_36_2","first-page":"16:1\u201316:39","article-title":"Scalable and accurate online feature selection for big data","volume":"11","author":"Yu Kui","year":"2016","unstructured":"Kui Yu, Xindong Wu, Wei Ding, and Jian Pei. 2016. Scalable and accurate online feature selection for big data. ACM Transactions on Knowledge Discovery from Data 11, 2 (2016), 16:1\u201316:39.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488055"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462413"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3494565"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.11.019"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273641"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v24i1.7671"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3591467","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3591467","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:48:47Z","timestamp":1750286927000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3591467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":41,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,9,30]]}},"alternative-id":["10.1145\/3591467"],"URL":"https:\/\/doi.org\/10.1145\/3591467","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,28]]},"assertion":[{"value":"2022-03-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-05","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}