{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:35:13Z","timestamp":1778585713954,"version":"3.51.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T00:00:00Z","timestamp":1633737600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T00:00:00Z","timestamp":1633737600000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s13042-021-01439-w","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T16:10:28Z","timestamp":1633882228000},"page":"1131-1144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semi-supervised label enhancement via structured semantic extraction"],"prefix":"10.1007","volume":"13","author":[{"given":"Tao","family":"Wen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9879-9855","authenticated-orcid":false,"given":"Xiuyi","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,9]]},"reference":[{"issue":"3","key":"1439_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas G, Katakis I (2007) Multi-label classification: an overview. Int J Data Warehous Min 3 (3):1\u201313","journal-title":"Int J Data Warehous Min"},{"key":"1439_CR2","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s11390-020-9900-z","volume":"35","author":"XY Jia","year":"2020","unstructured":"Jia XY, Zhu SS, Li WW (2020) Joint label-specific features and correlation information for multi-label learning. J Comput Sci Technol 35:247\u2013258","journal-title":"J Comput Sci Technol"},{"key":"1439_CR3","doi-asserted-by":"publisher","unstructured":"Li J, Zhang C, Zhou JT, Fu H, Xia S, Hu Q (2021) Deep-lift: deep label-specific feature learning for image annotation. IEEE Trans Cybern:1\u201310. https:\/\/doi.org\/10.1109\/TCYB.2021.3049630","DOI":"10.1109\/TCYB.2021.3049630"},{"key":"1439_CR4","doi-asserted-by":"crossref","unstructured":"Zhang F, Jia X, Li W (2020) Tensor-based multi-view label enhancement for multi-label learning. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence, pp 2369\u20132375","DOI":"10.24963\/ijcai.2020\/328"},{"issue":"7","key":"1439_CR5","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TKDE.2016.2545658","volume":"28","author":"X Geng","year":"2016","unstructured":"Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28 (7):1734\u20131748","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1439_CR6","doi-asserted-by":"crossref","unstructured":"Li YK, Zhang ML, Geng X (2015) Leveraging implicit relative labeling-importance information for effective multi-label learning. In: Proceedings of the IEEE International Conference on Data Mining, pp 251\u2013260","DOI":"10.1109\/ICDM.2015.41"},{"key":"1439_CR7","doi-asserted-by":"crossref","unstructured":"Hou P, Geng X, Zhang ML (2016) Multi-label manifold learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 1680\u20131686","DOI":"10.1609\/aaai.v30i1.10258"},{"key":"1439_CR8","doi-asserted-by":"crossref","unstructured":"Xu N, Tao A, Geng X (2018) Label enhancement for label distribution learning. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp 2926\u20132932","DOI":"10.24963\/ijcai.2018\/406"},{"key":"1439_CR9","doi-asserted-by":"crossref","unstructured":"Zhu W, Jia X, Li W (2020) Privileged label enhancement with multi-label learning. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp 2376\u20132382","DOI":"10.24963\/ijcai.2020\/329"},{"key":"1439_CR10","doi-asserted-by":"crossref","unstructured":"Tang H, Zhu J, Zheng Q, Wang J, Pang S, Li Z (2020) Label enhancement with sample correlations via low-rank representation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 5932\u20135939","DOI":"10.1609\/aaai.v34i04.6053"},{"key":"1439_CR11","unstructured":"Xu N, Shu J, Liu YP, Geng X (2020) Variational label enhancement. In: Proceedings of the International Conference on Machine Learning, pp 10597\u201310606"},{"issue":"1","key":"1439_CR12","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1016\/j.artint.2011.10.002","volume":"176","author":"ZH Zhou","year":"2012","unstructured":"Zhou ZH, Zhang ML, Huang SJ, Li YF (2012) Multi-instance multi-label learning. Artifi Intell 176 (1):2291\u20132320","journal-title":"Artifi Intell"},{"issue":"2","key":"1439_CR13","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","volume":"36","author":"HS Park","year":"2009","unstructured":"Park HS, Jun CH (2009) A simple and fast algorithm for k-medoids clustering. Expert Syst Appl 36 (2):3336\u20133341","journal-title":"Expert Syst Appl"},{"key":"1439_CR14","volume-title":"Measure, topology, and fractal geometry","author":"G Edgar","year":"2007","unstructured":"Edgar G (2007) Measure, topology, and fractal geometry. Springer, Berlin"},{"key":"1439_CR15","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.patrec.2019.06.012","volume":"125","author":"X Jia","year":"2019","unstructured":"Jia X, Ren T, Chen L, Wang J, Zhu J, Long X (2019) Weakly supervised label distribution learning based on transductive matrix completion with sample correlations. Pattern Recognit Lett 125:453\u2013462","journal-title":"Pattern Recognit Lett"},{"key":"1439_CR16","doi-asserted-by":"crossref","unstructured":"Ren T, Jia X, Li W, Zhao S (2019) Label distribution learning with label correlations via low-rank approximation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp 3325\u20133331","DOI":"10.24963\/ijcai.2019\/461"},{"issue":"4","key":"1439_CR17","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TKDE.2019.2943337","volume":"33","author":"X Jia","year":"2021","unstructured":"Jia X, Li Z, Zheng X, Li W, Huang SJ (2021) Label distribution learning with label correlations on local samples. IEEE Trans Knowl Data Eng 33 (4):1619\u20131631","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1439_CR18","unstructured":"Zhu X, Lafferty J, Rosenfeld R (2005) Semi-supervised learning with graphs. PhD thesis, Carnegie Mellon University, Language Technologies Institute, School of Computer Science"},{"key":"1439_CR19","volume-title":"Numerical recipes in C: the art of scientific computing","author":"WH Press","year":"1988","unstructured":"Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1988) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge"},{"issue":"1","key":"1439_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3 (1):1\u2013122","journal-title":"Found Trends Mach Learn"},{"issue":"4","key":"1439_CR21","first-page":"1956","volume":"20","author":"JF Cai","year":"2010","unstructured":"Cai JF, Cand\u00e8s EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. J Soc Ind Appl Math Optim 20 (4):1956\u20131982","journal-title":"J Soc Ind Appl Math Optim"},{"key":"1439_CR22","unstructured":"Lin Z, Chen M, Ma Y (2010) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:10095055"},{"issue":"4","key":"1439_CR23","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1137\/050626090","volume":"4","author":"PL Combettes","year":"2005","unstructured":"Combettes PL, Wajs VR (2005) Signal recovery by proximal forward-backward splitting. Multiscale Model Simul 4 (4):1168\u20131200","journal-title":"Multiscale Model Simul"},{"key":"1439_CR24","doi-asserted-by":"crossref","unstructured":"Wang L, Liu Y, Qin C, Sun G, Fu Y (2020) Dual relation semi-supervised multi-label learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 6227\u20136234","DOI":"10.1609\/aaai.v34i04.6089"},{"key":"1439_CR25","doi-asserted-by":"crossref","unstructured":"Zhan W, Zhang ML (2017) Inductive semi-supervised multi-label learning with co-training. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1305\u20131314","DOI":"10.1145\/3097983.3098141"},{"key":"1439_CR26","doi-asserted-by":"crossref","unstructured":"Sun L, Feng S, Lyu G, Lang C (2019) Robust semi-supervised multi-label learning by triple low-rank regularization. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 269\u2013280","DOI":"10.1007\/978-3-030-16145-3_21"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01439-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01439-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01439-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T16:31:56Z","timestamp":1673454716000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01439-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,9]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["1439"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01439-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,9]]},"assertion":[{"value":"14 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}