{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:26:55Z","timestamp":1768688815019,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62041604"],"award-info":[{"award-number":["62041604"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172198"],"award-info":[{"award-number":["62172198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61762064"],"award-info":[{"award-number":["61762064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62063029"],"award-info":[{"award-number":["62063029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20202BABL212005"],"award-info":[{"award-number":["20202BABL212005"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangxi Science Fund for Distinguished Young Scholars","award":["20192BCBL23001"],"award-info":[{"award-number":["20192BCBL23001"]}]},{"name":"Science and Technology Research Project of Jiangxi Provincial Education Department","award":["GJJ191152"],"award-info":[{"award-number":["GJJ191152"]}]},{"name":"Scientific Startup Foundation for Doctors of Nanchang Hangkong University","award":["EA202107235"],"award-info":[{"award-number":["EA202107235"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10489-022-03945-y","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T05:02:36Z","timestamp":1660021356000},"page":"9444-9462","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels"],"prefix":"10.1007","volume":"53","author":[{"given":"Zhi-Fen","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Hua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"issue":"5","key":"3945_CR1","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1109\/TNNLS.2013.2238682","volume":"24","author":"Y Luo","year":"2013","unstructured":"Luo Y, Tao DC, Xu C, Liu H, Wen YG (2013) Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans Neural Netw Learn Syst 24(5):709\u2013722","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"8","key":"3945_CR2","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1109\/TIP.2015.2421309","volume":"24","author":"Y Luo","year":"2015","unstructured":"Luo Y, Liu TL, Tao DC, Xu C (2015) Multiview matrix completion for multi-label image classification. IEEE Trans Image Process 24(8):2355\u20132368","journal-title":"IEEE Trans Image Process"},{"key":"3945_CR3","doi-asserted-by":"crossref","unstructured":"Liu M, Luo Y et al (2015) Low-rank multi-view learning in matrix completion for multi-label image classification. 29th AAAI Conference on Artificial Intelligence, pp 2778\u20132784","DOI":"10.1609\/aaai.v29i1.9547"},{"issue":"11","key":"3945_CR4","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.1109\/TMM.2020.2966887","volume":"22","author":"YS Zhang","year":"2020","unstructured":"Zhang YS, Wu J, Cai Z, Yu PS (2020) Multi-View Multi-Label Learning With Sparse Feature Selection for Image Annotation. IEEE Trans Multimed 22(11):2844\u20132857","journal-title":"IEEE Trans Multimed"},{"key":"3945_CR5","doi-asserted-by":"crossref","unstructured":"Wu X, Chen QG et al (2019) Multi-view multi-label learning with view-specific information extraction. 28th International Joint Conference on Artificial Intelligence, pp 3884\u20133890","DOI":"10.24963\/ijcai.2019\/539"},{"key":"3945_CR6","doi-asserted-by":"crossref","unstructured":"Chen ZS, Wu X, Chen QG, Hu Y, Zhang ML (2020) Multi-view partial multi-label learning with graph-based disambiguation. In: Proceedings of the 34th AAAI Conference on artificial intelligence (AAAI\u201920) New York, NY, pp 3553\u20133560","DOI":"10.1609\/aaai.v34i04.5761"},{"key":"3945_CR7","doi-asserted-by":"crossref","unstructured":"Wu JH, Wu X, Chen QG, Hu Y, Zhang ML (2020) Feature-induced manifold disambiguation for multi-view partial multi-label learning. In: Proceedings of the 26th ACM SIGKDD Conference on knowledge discovery and data mining (KDD\u201920), Virtual Event, pp 557\u2013565","DOI":"10.1145\/3394486.3403098"},{"key":"3945_CR8","doi-asserted-by":"publisher","first-page":"106841","DOI":"10.1016\/j.knosys.2021.106841","volume":"218","author":"DW Zhao","year":"2021","unstructured":"Zhao DW, Gao QW et al (2021) Consistency and Diversity neural network multi-view multi-label learning. Knowl Based Syst 218:106841","journal-title":"Knowl Based Syst"},{"issue":"3","key":"3945_CR9","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1109\/TCYB.2019.2950560","volume":"51","author":"QY Tan","year":"2021","unstructured":"Tan QY, Yu GX, Wang J et al (2021) Individuality- and commonality-based multiview multilabel learning. IEEE Trans Cybern 51(3):1716\u20131727","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"3945_CR10","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang ML, Zhou ZH (2007) ML-KNN: A lazy learning approach to multi-label learning. Pattern Recog 40(7):2038\u20132048","journal-title":"Pattern Recog"},{"issue":"19","key":"3945_CR11","doi-asserted-by":"publisher","first-page":"3218","DOI":"10.1016\/j.ins.2009.06.010","volume":"179","author":"ML Zhang","year":"2009","unstructured":"Zhang ML, Pen\u0308a JM , Robles V (2009) Feature selection for multi-label naive Bayes classification. Inf Sci 179(19):3218\u20133229","journal-title":"Inf Sci"},{"issue":"2","key":"3945_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10994-008-5064-8","volume":"73","author":"J F\u00fcrnkranz","year":"2008","unstructured":"F\u00fcrnkranz J, H\u00fcllermeier E, Mencia EL, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133\u2013153","journal-title":"Mach Learn"},{"issue":"2","key":"3945_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2499907.2499910","volume":"7","author":"Y Zhang","year":"2013","unstructured":"Zhang Y, Yeung DY (2013) Multilabel relationship learning. ACM Trans Knowl Discov Data 7(2):1\u201330","journal-title":"ACM Trans Knowl Discov Data"},{"key":"3945_CR14","doi-asserted-by":"crossref","unstructured":"Huang SJ, Yu Y, Zhou ZH (2012) Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD International conference on knowledge discovery and data mining, Beijing, China, pp 525\u2013533","DOI":"10.1145\/2339530.2339615"},{"key":"3945_CR15","unstructured":"Guo YH, Xue W (2013) Probabilistic multi-label classification with sparse feature learning. In: Proceedings of the 23rd International joint conference on artificial intelligence, pp 1373\u20131379"},{"key":"3945_CR16","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1007\/s10489-018-1345-5","volume":"49","author":"ZF He","year":"2019","unstructured":"He ZF , Yang M (2019) Sparse and low-rank representation for multi-label classification. Appl Intell 49:1708\u20131723","journal-title":"Appl Intell"},{"key":"3945_CR17","doi-asserted-by":"crossref","unstructured":"Ren WJY, Zhang L et al (2017) Robust mapping learning for multi-view multi-label classification with missing labels. International conference on knowledge science, engineering and management, Springer, pp 543\u2013551","DOI":"10.1007\/978-3-319-63558-3_46"},{"issue":"8","key":"3945_CR18","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"ML Zhang","year":"2014","unstructured":"Zhang ML, Zhou ZH (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":"3945_CR19","unstructured":"Yu HF, Jain P, Kar P, Dhillon IS (2014) Large-scale multi-label learning with missing labels. In: Proceedings of the 31st international conference on machine learning, pp 392\u2013 601"},{"issue":"10","key":"3945_CR20","doi-asserted-by":"publisher","first-page":"3405","DOI":"10.1016\/j.patcog.2014.04.009","volume":"47","author":"SF Wang","year":"2014","unstructured":"Wang SF, Wang J, Wang ZY, Ji Q (2014) Enhancing multi-label classification by modeling dependencies among labels. Pattern Recognit 47(10):3405\u20133413","journal-title":"Pattern Recognit"},{"key":"3945_CR21","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ins.2019.04.021","volume":"492","author":"J Huang","year":"2019","unstructured":"Huang J, Qin F, Zheng X et al (2019) Improving multi-label classification with missing labels by learning label-specific features. Inf Sci 492:124\u2013146","journal-title":"Inf Sci"},{"key":"3945_CR22","doi-asserted-by":"crossref","unstructured":"Bi W, Kwok JT (2014) Multilabel classification with label correlations and missing labels. In: Proceedings of the 28th AAAI Conference on artificial intelligence, pp 1680\u20131686","DOI":"10.1609\/aaai.v28i1.8996"},{"issue":"6","key":"3945_CR23","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1109\/TKDE.2017.2785795","volume":"30","author":"Y Zhu","year":"2018","unstructured":"Zhu Y, Kwok JT, Zhou ZH (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"},{"key":"3945_CR24","doi-asserted-by":"publisher","first-page":"4029","DOI":"10.1007\/s10489-020-01715-2","volume":"50","author":"ZW Cheng","year":"2020","unstructured":"Cheng ZW, Zeng ZW (2020) Joint label-specific features and label correlation for multi-label learning with missing label. Appl Intell 50:4029\u20134049","journal-title":"Appl Intell"},{"key":"3945_CR25","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.knosys.2018.08.018","volume":"163","author":"ZF He","year":"2019","unstructured":"He ZF, Yang M, Gao Y, Liu HD, Yin YL (2019) Joint multi-label classification and label correlations with missing labels and feature selection. Knowl Based Syst 163:145\u2013158","journal-title":"Knowl Based Syst"},{"key":"3945_CR26","doi-asserted-by":"crossref","unstructured":"Zhang CQ, Yu ZW et al (2018) Latent semantic aware multi-view multi-label classification. 32nd AAAI Conference on Artificial Intelligence, New Orleans, LA. pp 4414\u20134421","DOI":"10.1609\/aaai.v32i1.11722"},{"key":"3945_CR27","doi-asserted-by":"crossref","unstructured":"Wang GX, Zhang CQ, Zhu PF, Hu QH (2017) Semi-supervised multi-view multi-label classification based on nonnegative matrix factorization. International Conference on Artificial Neural Networks, pp 340\u2013348","DOI":"10.1007\/978-3-319-68612-7_39"},{"key":"3945_CR28","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1016\/j.neucom.2015.05.039","volume":"168","author":"ZY He","year":"2015","unstructured":"He ZY, Chen C, Bu JJ, Li P, Cai D (2015) Multi-view based multi-label propagation for image annotation. Neurocomputing 168:853\u2013860","journal-title":"Neurocomputing"},{"key":"3945_CR29","doi-asserted-by":"crossref","unstructured":"Zhang MY, Li CS, Wang ZF (2019) Multi-view metric learning for multi-label image classification. IEEE International Conference on Image Processing, pp 2134\u20132138","DOI":"10.1109\/ICIP.2019.8803160"},{"issue":"8","key":"3945_CR30","doi-asserted-by":"publisher","first-page":"2682","DOI":"10.1109\/TPAMI.2020.2974203","volume":"43","author":"SL Sun","year":"2021","unstructured":"Sun SL, Zong DM (2021) LCBM: A Multi-view probabilistic model for multi-label classification. IEEE Trans Pattern Anal Mach Intell 43(8):2682\u20132696","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3945_CR31","doi-asserted-by":"crossref","unstructured":"Zhu CM, Miao DQ et al (2019) Improved multi-view multi-label learning with incomplete views and labels. International conference on data mining workshops (ICDMW), Beijing, China, pp 689\u2013696","DOI":"10.1109\/ICDMW.2019.00104"},{"key":"3945_CR32","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.neucom.2019.09.009","volume":"371","author":"CM Zhu","year":"2020","unstructured":"Zhu CM, Miao DQ et al (2020) Global and local multi-view multi-label learning. Neurocomputing 371:67\u201377","journal-title":"Neurocomputing"},{"key":"3945_CR33","doi-asserted-by":"publisher","first-page":"15007","DOI":"10.1007\/s00521-020-04854-2","volume":"32","author":"CM Zhu","year":"2020","unstructured":"Zhu CM, Wang PH, Ma L et al (2020) Global and local multi-view multi-label learning with incomplete views and labels. Neural Comput Appl 32:15007\u201315028","journal-title":"Neural Comput Appl"},{"key":"3945_CR34","doi-asserted-by":"crossref","unstructured":"Tan QY, Yu GX, Domeniconi C, Wang J (2018) Multi-view weak-label learning based on matrix completion. In: Proceedings of the 2018 SIAM International conference on data mining, pp 450\u2013458","DOI":"10.1137\/1.9781611975321.51"},{"issue":"2","key":"3945_CR35","first-page":"107","volume":"102","author":"DW Zhao","year":"2021","unstructured":"Zhao DW, Gao QW, Lu YX, Sun D (2021) Two-step multi-view and multi-label learning with missing label via subspace learning. Appl Soft Comput 102(2):107\u2013120","journal-title":"Appl Soft Comput"},{"key":"3945_CR36","doi-asserted-by":"crossref","unstructured":"Tan QY, Yu GX, Domeniconi C, Wang J, Zhang ZL (2018) Incomplete multi-view weak-lebel learning. 27th International Joint Conference on Artificial Intelligence, pp 2703\u2013 2709","DOI":"10.24963\/ijcai.2018\/375"},{"key":"3945_CR37","unstructured":"Li X, Chen SC (2020) A concise yet effective model for non-aligned incomplete multi-view and missing multi-label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press"},{"key":"3945_CR38","doi-asserted-by":"crossref","unstructured":"Liu XY, Sun LJ, Feng SH (2021) Incomplete multi-view partial multi-label learning. Applied Intelligence. in press","DOI":"10.1007\/s10489-021-02606-w"},{"key":"3945_CR39","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Herbrich R, Smola AJ (2001) A generalized representer theorem. In: Helmbold, D, Williamson, B, eds Lecture notes in artificial intelligence 2111. Berlin: Springer-Verlag, pp 416\u2013426","DOI":"10.1007\/3-540-44581-1_27"},{"key":"3945_CR40","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"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03945-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03945-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03945-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T23:57:32Z","timestamp":1744156652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03945-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,9]]},"references-count":40,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["3945"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03945-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,9]]},"assertion":[{"value":"27 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}