{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T20:45:18Z","timestamp":1769201118125,"version":"3.49.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"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":["Grants 72401110, 72301144, and 72172057"],"award-info":[{"award-number":["Grants 72401110, 72301144, and 72172057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2682024CX053"],"award-info":[{"award-number":["2682024CX053"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["23KJB520009"],"award-info":[{"award-number":["23KJB520009"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            The goal of partial multi-label learning is to induce a multi-label classifier from partial multi-label data where each instance is annotated with a number of candidate labels but only a subset of them are valid. Many of the existing studies either fail to fully utilize instance and label correlations to eliminate noisy labels or build an over-simplified multi-label classifier, both of which are unfavorable for the improvement of generalization performance. In this article, we put forward a novel model named P\n            <jats:sc>ml-ilc<\/jats:sc>\n            to learn a multi-label classifier from partial multi-label data. Specifically, P\n            <jats:sc>ml-ilc<\/jats:sc>\n            first encodes instances and labels into a compact semantic space and takes full advantage of instance and label correlations to eliminate noisy labels. Then, it induces a linear mapping from the feature space to the label space while exploiting label-specific features and instance correlations to facilitate the multi-label classifier learning process. Finally, the above two steps are combined into a joint optimization problem and an efficient alternating optimization procedure is developed to find a satisfactory solution. Extensive experiments show that P\n            <jats:sc>ml-ilc<\/jats:sc>\n            achieves superior performance on both real-world and synthetic partial multi-label datasets in terms of different evaluation metrics.\n          <\/jats:p>","DOI":"10.1145\/3700879","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T15:02:06Z","timestamp":1729868526000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Partial Multi-Label Learning via Exploiting Instance and Label Correlations"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8035-5255","authenticated-orcid":false,"given":"Weichao","family":"Liang","sequence":"first","affiliation":[{"name":"Southwest Jiaotong University, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8183-2559","authenticated-orcid":false,"given":"Guangliang","family":"Gao","sequence":"additional","affiliation":[{"name":"Jiangsu Police Institute, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5537-8989","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing Forestry University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4726-7493","authenticated-orcid":false,"given":"Youquan","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Finance and Economics, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/303"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723401"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2359642"},{"issue":"42","key":"e_1_3_1_7_2","first-page":"1501","article-title":"Learning from Partial Labels","volume":"12","author":"Cour Timothee","year":"2011","unstructured":"Timothee Cour, Ben Sapp, and Ben Taskar. 2011. Learning from Partial Labels. Journal of Machine Learning Research 12, 42 (2011), 1501\u20131536.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2669639"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3109438"},{"key":"e_1_3_1_10_2","first-page":"181","volume-title":"Proceedings of the 15th IEEE International Conference on Data Mining","author":"Huang Jun","year":"2015","unstructured":"Jun Huang, Guorong Li, Qingming Huang, and Xindong Wu. 2015. Learning Label Specific Features for Multi-Label Classification. In Proceedings of the 15th IEEE International Conference on Data Mining, 181\u2013190."},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2608339"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.07.154"},{"key":"e_1_3_1_13_2","first-page":"1837","volume-title":"Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition","author":"Li Yuncheng","year":"2017","unstructured":"Yuncheng Li, Yale Song, and Jiebo Luo. 2017. Improving Pairwise Ranking for Multi-Label Image Classification. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, 1837\u20131845."},{"key":"e_1_3_1_14_2","first-page":"2612","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence","author":"Li Ziwei","year":"2020","unstructured":"Ziwei Li, Gengyu Lyu, and Songhe Feng. 2020. Partial Multi-Label Learning via Multi-Subspace Representation. 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