{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:09:18Z","timestamp":1772910558540,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T00:00:00Z","timestamp":1588723200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T00:00:00Z","timestamp":1588723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61503058"],"award-info":[{"award-number":["61503058"]}],"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":"crossref","award":["61702081"],"award-info":[{"award-number":["61702081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s13042-020-01129-z","type":"journal-article","created":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T22:02:30Z","timestamp":1588802550000},"page":"2453-2460","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Partial label metric learning by collapsing classes"],"prefix":"10.1007","volume":"11","author":[{"given":"Shuang","family":"Xu","sequence":"first","affiliation":[]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Ruirui","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Wenpeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jianjun","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,6]]},"reference":[{"key":"1129_CR1","doi-asserted-by":"crossref","unstructured":"Beygelzimer A, Langford J (2009) The offset tree for learning with partial labels. In: Proceedings of the 15th ACM sigkdd international conference on knowledge discovery and data mining. ACM, pp 129\u2013138","DOI":"10.1145\/1557019.1557040"},{"key":"1129_CR2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex optimization","author":"S Boyd","year":"2004","unstructured":"Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge"},{"issue":"7","key":"1129_CR3","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1109\/TPAMI.2017.2723401","volume":"40","author":"CH Chen","year":"2017","unstructured":"Chen CH, Patel VM, Chellappa R (2017) Learning from ambiguously labeled face images. IEEE Trans Pattern Anal Mach Intell 40(7):1653\u20131667","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"1129_CR4","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1109\/TIFS.2014.2359642","volume":"9","author":"YC Chen","year":"2014","unstructured":"Chen YC, Patel VM, Chellappa R, Phillips PJ (2014) Ambiguously labeled learning using dictionaries. IEEE Trans Inf Forensics Secur 9(12):2076\u20132088","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"3","key":"1129_CR5","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.patcog.2008.07.014","volume":"42","author":"E C\u00f4me","year":"2009","unstructured":"C\u00f4me E, Oukhellou L, Denoeux T, Aknin P (2009) Learning from partially supervised data using mixture models and belief functions. Pattern Recognit 42(3):334\u2013348","journal-title":"Pattern Recognit"},{"issue":"May","key":"1129_CR6","first-page":"1501","volume":"12","author":"T Cour","year":"2011","unstructured":"Cour T, Sapp B, Taskar B (2011) Learning from partial labels. J Mach Learn Res 12(May):1501\u20131536","journal-title":"J Mach Learn Res"},{"key":"1129_CR7","unstructured":"Dua D, Graff C (2019) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http:\/\/archive.ics.uci.edu\/ml"},{"key":"1129_CR8","unstructured":"Globerson A, Roweis ST (2006) Metric learning by collapsing classes. In: Advances in neural information processing systems, pp 451\u2013458"},{"key":"1129_CR9","unstructured":"Goldberger J, Hinton GE, Roweis ST, Salakhutdinov RR (2005) Neighbourhood components analysis. In: Advances in neural information processing systems, pp 513\u2013520"},{"issue":"3","key":"1129_CR10","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1109\/TCYB.2017.2669639","volume":"48","author":"C Gong","year":"2017","unstructured":"Gong C, Liu T, Tang Y, Yang J, Yang J, Tao D (2017) A regularization approach for instance-based superset label learning. IEEE Trans Cybern 48(3):967\u2013978","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"1129_CR11","doi-asserted-by":"crossref","first-page":"419","DOI":"10.3233\/IDA-2006-10503","volume":"10","author":"E H\u00fcllermeier","year":"2006","unstructured":"H\u00fcllermeier E, Beringer J (2006) Learning from ambiguously labeled examples. Intell Data Anal 10(5):419\u2013439","journal-title":"Intell Data Anal"},{"key":"1129_CR12","doi-asserted-by":"crossref","unstructured":"Li C, Zhang J, Chen Z (2013) Structured output learning with candidate labels for local parts. In: Joint European conference on machine learning and knowledge discovery in databases, pp 336\u2013352. Springer","DOI":"10.1007\/978-3-642-40991-2_22"},{"key":"1129_CR13","unstructured":"Liu L, Dietterich TG (2012) A conditional multinomial mixture model for superset label learning. In: Advances in neural information processing systems, pp 548\u2013556"},{"key":"1129_CR14","doi-asserted-by":"crossref","unstructured":"Liu Y, Gao Q, Han J, Wang S, Gao X (2019) Graph and autoencoder based feature extraction for zero-shot learning. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 15\u201336","DOI":"10.24963\/ijcai.2019\/421"},{"key":"1129_CR15","doi-asserted-by":"crossref","unstructured":"Liu Y, Gao Q, Li J, Han J, Shao L (2018) Zero shot learning via low-rank embedded semantic autoencoder. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 2490\u20132496","DOI":"10.24963\/ijcai.2018\/345"},{"issue":"2","key":"1129_CR16","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TIP.2016.2621667","volume":"26","author":"Y Liu","year":"2016","unstructured":"Liu Y, Gao Q, Miao S, Gao X, Nie F, Li Y (2016) A non-greedy algorithm for L1-norm LDA. IEEE Trans Image Process 26(2):684\u2013695","journal-title":"IEEE Trans Image Process"},{"key":"1129_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2019.08.023","volume":"121","author":"Y Liu","year":"2020","unstructured":"Liu Y, Gao X, Gao Q, Han J, Shao L (2020) Label-activating framework for zero-shot learning. Neural Netw 121:1\u20139","journal-title":"Neural Netw"},{"key":"1129_CR18","unstructured":"Luo J, Orabona F (2010) Learning from candidate labeling sets. In: Advances in neural information processing systems, pp 1504\u20131512"},{"key":"1129_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2933837","author":"G Lyu","year":"2019","unstructured":"Lyu G, Feng S, Wang T, Lang C, Li Y (2019) GM-PLL: Graph matching based partial label learning. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2019.2933837","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1129_CR20","doi-asserted-by":"crossref","unstructured":"Nguyen N, Caruana R (2008) Classification with partial labels. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 551\u2013559","DOI":"10.1145\/1401890.1401958"},{"issue":"Apr","key":"1129_CR21","first-page":"1297","volume":"11","author":"VC Raykar","year":"2010","unstructured":"Raykar VC, Yu S, Zhao LH, Valadez GH, Florin C, Bogoni L, Moy L (2010) Learning from crowds. J Mach Learn Res 11(Apr):1297\u20131322","journal-title":"J Mach Learn Res"},{"key":"1129_CR22","doi-asserted-by":"crossref","unstructured":"Song J, Liu H, Geng F, Zhang C (2016) Weakly-supervised classification of pulmonary nodules based on shape characters. In: the 14th international conference on dependable, autonomic and secure computing. IEEE, pp 228\u2013232","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.58"},{"key":"1129_CR23","doi-asserted-by":"crossref","unstructured":"Tang CZ, Zhang ML (2017) Confidence-rated discriminative partial label learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10775"},{"key":"1129_CR24","doi-asserted-by":"crossref","unstructured":"Verma Y, Jawahar C (2012) Image annotation using metric learning in semantic neighbourhoods. In: European conference on computer vision. Springer, pp 836\u2013849","DOI":"10.1007\/978-3-642-33712-3_60"},{"key":"1129_CR25","unstructured":"Wang S, Jin R (2009) An information geometry approach for distance metric learning. In: Artificial intelligence and statistics, pp 591\u2013598"},{"issue":"Feb","key":"1129_CR26","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10(Feb):207\u2013244","journal-title":"J Mach Learn Res"},{"key":"1129_CR27","doi-asserted-by":"crossref","unstructured":"Wisniewski G, P\u00e9cheux N, Gahbiche-Braham S, Yvon F (2014) Cross-lingual part-of-speech tagging through ambiguous learning. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1779\u20131785","DOI":"10.3115\/v1\/D14-1187"},{"issue":"6","key":"1129_CR28","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1109\/TKDE.2017.2788430","volume":"30","author":"J Wu","year":"2018","unstructured":"Wu J, Pan S, Zhu X, Zhang C, Wu X (2018) Multi-instance learning with discriminative bag mapping. IEEE Trans Knowl Data Eng 30(6):1065\u20131080","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1129_CR29","unstructured":"Xing EP, Jordan MI, Russell SJ, Ng AY (2003) Distance metric learning with application to clustering with side-information. In: Advances in neural information processing systems, pp 521\u2013528"},{"key":"1129_CR30","doi-asserted-by":"crossref","unstructured":"Xu BC, Ting KM, Zhou ZH (2019) Isolation set-kernel and its application to multi-instance learning. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 941\u2013949","DOI":"10.1145\/3292500.3330830"},{"key":"1129_CR31","doi-asserted-by":"crossref","unstructured":"Xu N, Lv J, Geng X (2019) Partial label learning via label enhancement. In: AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v33i01.33015557"},{"key":"1129_CR32","unstructured":"Yu F, Zhang ML (2016) Maximum margin partial label learning. In: Asian conference on machine learning, pp 96\u2013111"},{"key":"1129_CR33","unstructured":"Zhang ML, Yu F (2015) Solving the partial label learning problem: An instance-based approach. In: Twenty-fourth international joint conference on artificial intelligence"},{"issue":"10","key":"1129_CR34","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1109\/TKDE.2017.2721942","volume":"29","author":"ML Zhang","year":"2017","unstructured":"Zhang ML, Yu F, Tang CZ (2017) Disambiguation-free partial label learning. IEEE Trans Knowl Data Eng 29(10):2155\u20132167","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1129_CR35","doi-asserted-by":"crossref","unstructured":"Zhang ML, Zhou BB, Liu XY (2016) Partial label learning via feature-aware disambiguation. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1335\u20131344","DOI":"10.1145\/2939672.2939788"},{"issue":"28","key":"1129_CR36","first-page":"109","volume":"18","author":"S Zhang","year":"2018","unstructured":"Zhang S, Chai J (2018) Partial label learning algorithm based on maximum margin. Sci Technol Eng 18(28):109\u2013115","journal-title":"Sci Technol Eng"},{"key":"1129_CR37","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.neucom.2017.08.058","volume":"275","author":"Y Zhou","year":"2018","unstructured":"Zhou Y, Gu H (2018) Geometric mean metric learning for partial label data. Neurocomputing 275:394\u2013402","journal-title":"Neurocomputing"},{"issue":"12","key":"1129_CR38","doi-asserted-by":"crossref","first-page":"4443","DOI":"10.1109\/TCYB.2016.2611534","volume":"47","author":"Y Zhou","year":"2016","unstructured":"Zhou Y, He J, Gu H (2016) Partial label learning via gaussian processes. IEEE Trans Cybernet 47(12):4443\u20134450","journal-title":"IEEE Trans Cybernet"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01129-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-020-01129-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01129-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T06:12:32Z","timestamp":1666505552000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-020-01129-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,6]]},"references-count":38,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["1129"],"URL":"https:\/\/doi.org\/10.1007\/s13042-020-01129-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,6]]},"assertion":[{"value":"19 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}