Dual-Information Driven Deep Multi-View Clustering for Heterogeneous Data

Dual-Information Driven Deep Multi-View Clustering for Heterogeneous Data | 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 Dual-Information Driven Deep Multi-View Clustering for Heterogeneous Data Chunzhu Xie, Jun Kong, Min Jiang, Xuefeng Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6199242/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 13 You are reading this latest preprint version Abstract Deep multi-view clustering (DMVC) aims to utilize the consistency of multi-view data to learn a consensus representation using deep learning-based methods. However, existing methods overlook the presence of both semantic feature and topological structure information in the data. Also, the importance of these two information varies for heterogeneous data. To address these issues, we propose Dual-Information Driven Deep Multi-View Clustering for Heterogeneous Data (DID-DMVC). Firstly, to capture both semantic feature and topological structure information, we design a Dual-Information Extractor (DIE), which independently extracts two types of information. Secondly, we have designed a Tensor-Guided Low-Rank Fusion (TGLRF) strategy and developed a Dual-Level Adaptive Fusion Module (DLAFM). It adapts the fusion process by considering both the importance between views and the importance of the two different types of information for heterogeneous data. Thirdly, we design an Auxiliary Contrastive Loss (ACL) to regulate discrepancies between semantic feature and topological structure information during the DLAFM process. Finally, experiments demonstrate our model’s applicability across various types of datasets. Deep multi-view clustering topological structure information semantic feature information heterogeneous data. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 28 May, 2025 Reviews received at journal 18 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviews received at journal 19 Mar, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviewers agreed at journal 16 Mar, 2025 Reviewers agreed at journal 15 Mar, 2025 Reviewers invited by journal 15 Mar, 2025 Editor assigned by journal 15 Mar, 2025 Submission checks completed at journal 12 Mar, 2025 First submitted to journal 10 Mar, 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. 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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-6199242","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":429203301,"identity":"d6d4c87e-1e82-4bf3-b2af-0d03ad6c88f1","order_by":0,"name":"Chunzhu Xie","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Chunzhu","middleName":"","lastName":"Xie","suffix":""},{"id":429203302,"identity":"d784e215-e4be-402a-bdc6-6802c7304114","order_by":1,"name":"Jun Kong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACCSghx9iMEDQgSosxiVqAILEBSRC/Fsn23sMvLNvupDe3Mz97+HWHXR4De/M2CYaaOzi1SPOcS7OQbHuW29jMZm4seya5mIHnWJkEw7FnOLXISeSYGUi2HQZqYTCTlmxjTmwAikgwNhwmqCWdsZn9G1BLfWKD/Bv8WqQlcowfALUkMDbzmEl+bDsMtIUHvxbJnjNmDBLnDhs2NvOUSTO2HU9s40krtkg4hluLxPEe488SZYflDfuPb5P82Vad2M9+eOONDzW4tQABmzQocgwbGBiYeUBckFgCPg1AhR8/AEl5IGb8gV/lKBgFo2AUjFAAAKLwUCvPXM8bAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangnan University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Kong","suffix":""},{"id":429203303,"identity":"80a572d8-9719-4398-bac3-41f62129ea13","order_by":2,"name":"Min Jiang","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Jiang","suffix":""},{"id":429203304,"identity":"e9d6899c-b3d2-4d21-9344-d9b4781694eb","order_by":3,"name":"Xuefeng Tao","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2025-03-11 01:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6199242/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6199242/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-026-02996-8","type":"published","date":"2026-02-16T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103251087,"identity":"374b96f5-a0e8-472e-af10-9ff86cc8d3ef","added_by":"auto","created_at":"2026-02-23 16:04:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699183,"visible":true,"origin":"","legend":"","description":"","filename":"DIDDMVCHD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6199242/v1_covered_3b23a017-97ab-4bcd-8b29-011e69b42e5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-Information Driven Deep Multi-View Clustering for Heterogeneous Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Deep multi-view clustering, topological structure information, semantic feature information, heterogeneous data.","lastPublishedDoi":"10.21203/rs.3.rs-6199242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6199242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDeep multi-view clustering (DMVC) aims to utilize the consistency of multi-view data to learn a consensus representation using deep learning-based methods. 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