{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T22:09:58Z","timestamp":1787004598846,"version":"build-2736575974"},"reference-count":44,"publisher":"Institution of Engineering and Technology (IET)","issue":"3","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62506223"],"award-info":[{"award-number":["62506223"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CAAI Trans on Intel Tech"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Partial Multi\u2010label Learning (PML) deals with the ambiguity where each instance is annotated with a set of candidate labels, and only a subset of which is valid. While existing PML methods focus primarily on label disambiguation, they often rely on the assumption of a clean feature space. However, in real\u2010world applications, data are frequently plagued by the co\u2010existence of label noise and feature noise, referred to as the dual noise challenge. Consequently, model robustness degrades substantially. To address this, we propose a framework named Ranking\u2010Consistent Correntropy\u2010based subspace learning for Partial Multi\u2010label Learning (RCC\u2010PML). Unlike existing dual noise PML methods that operate in the input space, our work introduces a subspace learning framework, where robust representation and semantic ranking are jointly optimized to enforce cross\u2010space consistency. Specifically, we leverage the Maximum Correntropy Criterion (MCC) to construct robust scatter matrices, effectively suppressing heavy\u2010tailed feature noise. To tackle label ambiguity, a ranking\u2010consistent constraint is introduced to encourage a reasonable margin between ground\u2010truth and false\u2010positive labels in the projected subspace. Furthermore, we incorporate dual\u2010graph regularization to preserve both the local manifold structure via anchor embedding and global semantic consistency. Finally,\n                    <jats:italic>L<\/jats:italic>\n                    <jats:sub>2,1<\/jats:sub>\n                    \u2010norm regularization is imposed on the projection matrix to perform adaptive feature selection. Extensive experiments on benchmark datasets demonstrate that the proposed method significantly outperforms state\u2010of\u2010the\u2010art algorithms, particularly in heavy\u2010tailed environments.\n                  <\/jats:p>","DOI":"10.1049\/cit2.70134","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:45:41Z","timestamp":1776883541000},"page":"754-768","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Partial Multi\u2010Label Learning Under Dual Noise via Joint Subspace Learning"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5059-9854","authenticated-orcid":false,"given":"Yuanjian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science Shanghai University  Shanghai 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Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Witold","family":"Pedrycz","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering University of Alberta  Edmonton Alberta Canada"},{"name":"Institute of Systems Engineering Macau University of Science and Technology  Macau China"},{"name":"Research Center of Performance and Productivity Analysis Istinye University  Istanbul T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.39"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3119334"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.112757"},{"key":"e_1_2_10_5_1","first-page":"4302","volume-title":"Proceedings of the Thirty\u2010Second AAAI Conference on Artificial Intelligence, 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