GLRE: Low-Rank Regularized Label Enhancement with Manifold Constraints

GLRE: Low-Rank Regularized Label Enhancement with Manifold Constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GLRE: Low-Rank Regularized Label Enhancement with Manifold Constraints Yifan Fang, Lizhou Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8395793/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Label Distribution Learning (LDL) effectively handles label ambiguity but is often constrained by the difficulty of acquiring ground-truth distributions. Label Enhancement (LE) addresses this by recovering latent distributions from logical labels; however, existing graph-based methods typically overlook global semantic correlations among labels and remain sensitive to annotation noise . To bridge this gap, this paper proposes Global-Local Regularized Enhancement (GLRE), a unified optimization framework that integrates graph Laplacian regularization with nuclear norm minimization. This dual-constraint mechanism allows GLRE to simultaneously capture the local geometry of the feature manifold and the global low-rank structure of the label space . Furthermore, to tackle the scalability challenge, we develop an efficient ADMM-based solver accelerated by the Conjugate Gradient (CG) method . Extensive experiments on four benchmark datasets demonstrate that GLRE yields competitive performance, achieving KL divergence reductions of approximately 30%-60%, particularly in small-sample and high-noise scenarios compared to existing representative baselines. Label Distribution Learning Label Enhancement Low-Rank Representation Manifold Regularization ADMM Conjugate Gradient Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 21 Dec, 2025 Submission checks completed at journal 19 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8395793","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589940519,"identity":"22af0118-cec4-4579-aaa8-8d20a0ed76d3","order_by":0,"name":"Yifan Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACg8OMDxgY/wFZzOwHDkhUSMjJE9bCbMDA2ABksfckPrA4Y2Fs2EBAi2QDTAvPAWODyraKRIYDBLTwszMzfi5ssMuTj0hIk7g5TyKBsYH54aMbeLSwMTMzS8/8kVxseCPxmOTMbRJ57AxsxsY5eLXwH5DmbWBO3DgjIU1acptEMWMDD5s0fi3MzL95G+pBWsyk/86RSGw4QFgLG9CWw4nzQd6XbCBSizVvw/HEDaBAljgmYWzYTMgv/IeZb/M2VCfObwZFZU2dnDx788PH+LTAgcEBGIuZGOUgIN9ArMpRMApGwSgYcQAA3q9IoDkcTc4AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Fang","suffix":""},{"id":589940520,"identity":"249a2fcd-05de-47c6-b81b-cb75b08a0a16","order_by":1,"name":"Lizhou Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lizhou","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-12-18 13:08:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8395793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8395793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963685,"identity":"88cd27da-766a-43c6-8872-01ab5f298725","added_by":"auto","created_at":"2026-02-19 04:20:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1861337,"visible":true,"origin":"","legend":"","description":"","filename":"GLRELATEX.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8395793/v1_covered_53cc6d0a-8d97-4cf1-b845-d88b9a987498.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GLRE: Low-Rank Regularized Label Enhancement with Manifold Constraints","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Label Distribution Learning, Label Enhancement, Low-Rank Representation, Manifold Regularization, ADMM, Conjugate Gradient","lastPublishedDoi":"10.21203/rs.3.rs-8395793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8395793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLabel Distribution Learning (LDL) effectively handles label ambiguity but is often constrained by the difficulty of acquiring ground-truth distributions. 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