{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T19:52:18Z","timestamp":1778961138907,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s10489-021-02606-w","type":"journal-article","created":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T09:02:30Z","timestamp":1625302950000},"page":"3289-3302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Incomplete multi-view partial multi-label learning"],"prefix":"10.1007","volume":"52","author":[{"given":"Xinyuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-9358","authenticated-orcid":false,"given":"Songhe","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,3]]},"reference":[{"issue":"3","key":"2606_CR1","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1057\/palgrave.jors.2600425","volume":"48","author":"D Bertsekas","year":"1997","unstructured":"Bertsekas D (1997) Nonlinear programming. J Oper Res Soc 48(3):334\u2013334","journal-title":"J Oper Res Soc"},{"key":"2606_CR2","unstructured":"Boyd S, Vandenberghe L (2014) Convex Optimization. Cambridge University Press"},{"issue":"4","key":"2606_CR3","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1137\/080738970","volume":"20","author":"J Cai","year":"2010","unstructured":"Cai J, Cand\u0117s E J, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956\u2013 1982","journal-title":"SIAM J Optim"},{"key":"2606_CR4","doi-asserted-by":"crossref","unstructured":"Chen Z, Wu X, Chen Q, Hu Y, Zhang M (2020) Multi-view partial multi-label learning with graph-based disambiguation. In: AAAI Conference on Artificial Intelligence, pp 3553\u20133560","DOI":"10.1609\/aaai.v34i04.5761"},{"key":"2606_CR5","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"2606_CR6","doi-asserted-by":"crossref","unstructured":"Fang J, Zhang M (2019) Partial multi-label learning via credible label elicitation. In: AAAI Conference on Artificial Intelligence, pp 3518\u20133525","DOI":"10.1609\/aaai.v33i01.33013518"},{"key":"2606_CR7","doi-asserted-by":"crossref","unstructured":"Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surv 47(3):52:1\u201352:38","DOI":"10.1145\/2716262"},{"key":"2606_CR8","doi-asserted-by":"crossref","unstructured":"Guillaumin M, Verbeek JJ, Schmid C (2010) Multimodal semi-supervised learning for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, pp 902\u2013909","DOI":"10.1109\/CVPR.2010.5540120"},{"key":"2606_CR9","doi-asserted-by":"crossref","unstructured":"He S, Deng K, Li L, Shu S, Liu L (2019) Discriminatively relabel for partial multi-label learning. In: IEEE International Conference on Data Mining, pp 280\u2013288","DOI":"10.1109\/ICDM.2019.00038"},{"key":"2606_CR10","doi-asserted-by":"crossref","unstructured":"Li Z, Lyu G, Feng S (2020) Partial multi-label learning via multi-subspace representation. In: Proceedings of International Joint Conference on Artificial Intelligence, pp 2612\u20132618","DOI":"10.24963\/ijcai.2020\/362"},{"key":"2606_CR11","unstructured":"Lin Z, Liu R, Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. In: Annual Conference on Neural Information Processing Systems, pp 612\u2013620"},{"issue":"1","key":"2606_CR12","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","volume":"35","author":"G Liu","year":"2013","unstructured":"Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171\u2013184","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2606_CR13","doi-asserted-by":"crossref","unstructured":"Lyu G, Feng S, Li Y (2020) Partial multi-label learning via probabilistic graph matching mechanism. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, pp 105\u2013113","DOI":"10.1145\/3394486.3403053"},{"key":"2606_CR14","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.ins.2020.09.019","volume":"543","author":"G Lyu","year":"2021","unstructured":"Lyu G, Feng S, Li Y (2021) Noisy label tolerance: A new perspective of partial multi-label learning. Inf Sci 543:454\u2013466","journal-title":"Inf Sci"},{"key":"2606_CR15","doi-asserted-by":"crossref","unstructured":"Sun L, Feng S, Wang T, Lang C, Jin Y (2019) Partial multi-label learning by low-rank and sparse decomposition. In: AAAI Conference on Artificial Intelligence, pp 5016\u20135023","DOI":"10.1609\/aaai.v33i01.33015016"},{"issue":"2","key":"2606_CR16","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s10115-020-01527-3","volume":"63","author":"L Sun","year":"2021","unstructured":"Sun L, Feng S, Lyu G, Zhang H, Dai G (2021) Partial multi-label learning with noisy side information. Knowl Inf Syst 63(2):541\u2013564","journal-title":"Knowl Inf Syst"},{"key":"2606_CR17","doi-asserted-by":"crossref","unstructured":"Sun S, Zong D (2020) Lcbm: a multi-view probabilistic model for multi-label classification. IEEE Trans Pattern Anal Mach Intell:1\u20131","DOI":"10.1109\/TPAMI.2021.3136965"},{"key":"2606_CR18","doi-asserted-by":"crossref","unstructured":"Tan Q, Yu G, Domeniconi C, Wang J, Zhang Z (2018) Incomplete multi-view weak-label learning. In: Proceedings of International Joint Conference on Artificial Intelligence, pp 2703\u20132709","DOI":"10.24963\/ijcai.2018\/375"},{"key":"2606_CR19","unstructured":"Tan Q, Yu G, Wang J, Domeniconi C, Zhang X (2019) Individuality- and commonality-based multiview multilabel learning. IEEE Trans. Cybern. PP(99):1\u201312"},{"key":"2606_CR20","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.knosys.2018.10.022","volume":"163","author":"H Wang","year":"2019","unstructured":"Wang H, Yang Y, Liu B, Fujita H (2019) A study of graph-based system for multi-view clustering. Knowl Based Syst 163:1009\u20131019","journal-title":"Knowl Based Syst"},{"issue":"6","key":"2606_CR21","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","volume":"25","author":"Y Wang","year":"2013","unstructured":"Wang Y, Zhang Y (2013) Nonnegative matrix factorization: A comprehensive review. IEEE Trans Knowl Data Eng 25(6):1336\u20131353","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2606_CR22","doi-asserted-by":"crossref","unstructured":"Wu J, Wu X, Chen Q, Hu Y, Zhang M (2020) Feature-induced manifold disambiguation for multi-view partial multi-label learning. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 557\u2013565","DOI":"10.1145\/3394486.3403098"},{"key":"2606_CR23","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.knosys.2019.03.023","volume":"175","author":"Q Xiao","year":"2019","unstructured":"Xiao Q, Dai J, Luo J, Fujita H (2019) Multi-view manifold regularized learning-based method for prioritizing candidate disease mirnas. Knowl Based Syst 175:118\u2013129","journal-title":"Knowl Based Syst"},{"key":"2606_CR24","doi-asserted-by":"crossref","unstructured":"Xie M, Huang S (2018) Partial multi-label learning. In: Proceedings of AAAI Conference on Artificial Intelligence, pp 4302\u20134309","DOI":"10.1609\/aaai.v32i1.11644"},{"key":"2606_CR25","doi-asserted-by":"crossref","unstructured":"Xie M, Huang S (2020) Partial multi-label learning with noisy label identification. In: AAAI Conference on Artificial Intelligence, pp 6454\u20136461","DOI":"10.1609\/aaai.v34i04.6117"},{"issue":"12","key":"2606_CR26","doi-asserted-by":"publisher","first-page":"5812","DOI":"10.1109\/TIP.2015.2490539","volume":"24","author":"C Xu","year":"2015","unstructured":"Xu C, Tao D, Xu C (2015) Multi-view learning with incomplete views. IEEE Trans Image Process 24(12):5812\u20135825","journal-title":"IEEE Trans Image Process"},{"key":"2606_CR27","doi-asserted-by":"crossref","unstructured":"Xu N, Liu Y, Geng X (2020) Partial multi-label learning with label distribution. In: The AAAI Conference on Artificial Intelligence, pp 6510\u20136517","DOI":"10.1609\/aaai.v34i04.6124"},{"key":"2606_CR28","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1016\/j.knosys.2018.10.001","volume":"163","author":"Z Yi","year":"2019","unstructured":"Yi Z, Yang Y, Li T, Fujita H (2019) A multitask multiview clustering algorithm in heterogeneous situations based on LLE and LE. Knowl Based Syst 163:776\u2013786","journal-title":"Knowl Based Syst"},{"key":"2606_CR29","doi-asserted-by":"crossref","unstructured":"Yu G, Chen X, Domeniconi C, Wang J, Li Z, Zhang Z, Wu X (2018) Feature-induced partial multi-label learning. In: IEEE International Conference on Data Mining, pp 1398\u2013 1403","DOI":"10.1109\/ICDM.2018.00192"},{"key":"2606_CR30","doi-asserted-by":"crossref","unstructured":"Zhang B, Qiang Q, Wang F, Nie F (2020a) Fast multi-view semi-supervised learning with learned graph. IEEE Trans Knowl Data Eng:1\u20131","DOI":"10.1109\/TKDE.2019.2953668"},{"issue":"8","key":"2606_CR31","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M Zhang","year":"2014","unstructured":"Zhang M, Zhou Z (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2606_CR32","doi-asserted-by":"crossref","unstructured":"Zhang Q, Zhong Y, Zhang M (2018) Feature-induced labeling information enrichment for multi-label learning. In: Mcilraith SA, Weinberger K Q (eds) Proceedings of AAAI Conference on Artificial Intelligence. AAAI Press, pp 4446\u20134453","DOI":"10.1609\/aaai.v32i1.11656"},{"key":"2606_CR33","doi-asserted-by":"publisher","first-page":"105895","DOI":"10.1016\/j.cmpb.2020.105895","volume":"199","author":"X Zhang","year":"2021","unstructured":"Zhang X, Yang Y, Li T, Zhang Y, Wang H, Fujita H (2021) CMC: A consensus multi-view clustering model for predicting alzheimer\u2019s disease progression. Comput Methods Programs Biomed 199:105895","journal-title":"Comput Methods Programs Biomed"},{"key":"2606_CR34","doi-asserted-by":"crossref","unstructured":"Zhang Y, Shi D, Gao J, Cheng D (2017) Low-rank-sparse subspace representation for robust regression. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2972\u20132981","DOI":"10.1109\/CVPR.2017.317"},{"issue":"11","key":"2606_CR35","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.1109\/TMM.2020.2966887","volume":"22","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wu J, Cai Z, Yu P S (2020b) Multi-view multi-label learning with sparse feature selection for image annotation. IEEE Trans Multimed 22(11):2844\u20132857","journal-title":"IEEE Trans Multimed"},{"issue":"6","key":"2606_CR36","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1109\/TKDE.2017.2785795","volume":"30","author":"Y Zhu","year":"2018","unstructured":"Zhu Y, Kwok J T, Zhou Z (2018) Multi-label learning with global and local label correlation. IEEE Trans Knowl Data Eng 30(6):1081\u20131094","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02606-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02606-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02606-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T17:30:29Z","timestamp":1672680629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02606-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,3]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["2606"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02606-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,3]]},"assertion":[{"value":"10 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}