Correntropy based Low-rank Matrix Factorization with Multi-view Constraint Graph Learning for Multi-view Data Clustering

Correntropy based Low-rank Matrix Factorization with Multi-view Constraint Graph Learning for Multi-view Data Clustering | 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 Correntropy based Low-rank Matrix Factorization with Multi-view Constraint Graph Learning for Multi-view Data Clustering Yuanhua Du, Pan Chen, Nan Zhou, Eryang Chen, Kaibo Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4785417/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the increase in data pattern diversity, multi-view clustering has become a hot topic for researchers. However, most of the current multi-view clustering methods focus on unsupervised learning scenarios, which cannot utilize the label information in the data. Furthermore, they could not handle the outliers, which may exist in the data. In order to address these issues, this paper proposes a Correntropy-based Multi-view Low-rank Matrix Factorization (CMLMF) method for multi-view data semi-supervised clustering. In order to sufficiently utilize the local structure information, a correntropy-based multi-view constraint graph learning framework is proposed to extract the local structure hidden in the multi-view data adaptively. Furthermore, a correntropy-based multi-view Low-rank Matrix Factorization (LMF) model is proposed, which can be incorporated with the adaptive graph learning framework to extract the global reconstruction information of data. The label information is introduced by a constraint matrix, which can be utilized for both global reconstruction and local structure information learning without adding any hyperparameter. Due to the maximum correntropy criterion, the proposed method is robust against the outliers in the data in each view and helps to extract a more current consensus representation. An efficient solving algorithm based on accelerated Block Coordinate Update (BCU) is designed to solve the model. Experiments are conducted on five real-world datasets, and the proposed method is compared with ten state-of-the-art methods to evaluate the effectiveness of the CMLMF method. The results show that the proposed method performs better clustering in most cases. Low-rank matrix factorization semi-supervised learning (SSL) multi-view clustering maximum correntropy criterion (MCC) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4785417","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334502988,"identity":"69df99d0-8bb4-44b3-b0e6-2683b3b3bef7","order_by":0,"name":"Yuanhua Du","email":"","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuanhua","middleName":"","lastName":"Du","suffix":""},{"id":334502989,"identity":"878e0120-d3b3-4ded-98be-67010203d021","order_by":1,"name":"Pan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYDACZgTrwIEPP4jTwtgAYbElHpzZQ5w9MC08xoc52IhQb3Cc+fmDj3sOM/DP7vlwmIGHQZ5f7AABLYfZDBtnPDvMIHHn7IbDBRYMhjNnJ+DXItnMw9jMc+Awg4FE7obDM3gYEgxuE68l58FhHjYitPAzI7QwEKuFzXDmjAPpQL8cMwAGsgRhv7DxH37w4cMBa2CINT/+8OGHjTy/NAEtUNBc3yABZkgQpRwE6khRPApGwSgYBSMNAACgzUNEgvmpBgAAAABJRU5ErkJggg==","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Pan","middleName":"","lastName":"Chen","suffix":""},{"id":334502990,"identity":"270a12cd-0c73-4a6b-ab92-10bacbf8de85","order_by":2,"name":"Nan Zhou","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Zhou","suffix":""},{"id":334502991,"identity":"51be5c24-d43c-43de-9aa4-0e73d7c85e41","order_by":3,"name":"Eryang Chen","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Eryang","middleName":"","lastName":"Chen","suffix":""},{"id":334502992,"identity":"74f1e808-126f-4999-a64c-8c3bbaac6937","order_by":4,"name":"Kaibo Shi","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Kaibo","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-07-23 03:54:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4785417/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4785417/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64001700,"identity":"3da36fd5-c6a6-491a-803c-1c42ca2fda64","added_by":"auto","created_at":"2024-09-04 19:46:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":651504,"visible":true,"origin":"","legend":"","description":"","filename":"snCMLMFzn.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4785417/v1_covered_03899d62-2a8b-4b61-a701-6168031a336d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correntropy based Low-rank Matrix Factorization with Multi-view Constraint Graph Learning for Multi-view Data Clustering","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Low-rank matrix factorization, semi-supervised learning (SSL), multi-view clustering, maximum correntropy criterion (MCC)","lastPublishedDoi":"10.21203/rs.3.rs-4785417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4785417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the increase in data pattern diversity, multi-view clustering has become a hot topic for researchers. However, most of the current multi-view clustering methods focus on unsupervised learning scenarios, which cannot utilize the label information in the data. Furthermore, they could not handle the outliers, which may exist in the data. In order to address these issues, this paper proposes a Correntropy-based Multi-view Low-rank Matrix Factorization (CMLMF) method for multi-view data semi-supervised clustering. In order to sufficiently utilize the local structure information, a correntropy-based multi-view constraint graph learning framework is proposed to extract the local structure hidden in the multi-view data adaptively. Furthermore, a correntropy-based multi-view Low-rank Matrix Factorization (LMF) model is proposed, which can be incorporated with the adaptive graph learning framework to extract the global reconstruction information of data. 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