Multi-view Unsupervised Feature Selection Guided by Latent Representation and Tensor Learning

Multi-view Unsupervised Feature Selection Guided by Latent Representation and Tensor Learning | 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 Multi-view Unsupervised Feature Selection Guided by Latent Representation and Tensor Learning Jianjun Jiang, Xijiong Xie, Guoqing Chao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7158182/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 Multi-view unsupervised feature selection refers to the process of selecting the most representative or relevant feature subset from a dataset containing multiple views. Most existing methods in this field use the original features to construct similarity graphs, but real datasets often contain noise and redundant information. The method of constructing adaptive graphs and spectral embedding both obtain the local structure of data and ignore the global information. On the other hand, they usually obtain consistency information through multi-graph fusion strategies or the construction of consistency graphs. However, due to the heterogeneity of view features, such methods are prone to lose original information and it is difficult to identify shared similar structures. To address these problems, we propose a model named Multi-view Unsupervised Feature Selection Guided by Latent Representation and Tensor Learning (TLMvFS). Our approach maps raw data into a latent space to capture semantic structural information. Subspace learning is then applied to the novel latent representation matrix to eliminate irrelevant features, enabling a more accurate capture of the intrinsic data structure. Currently, spectral representation is introduced to preserve shared local structures. To model correlations across multiple views, we adopt low-rank tensor learning to characterize high-order correlations. Furthermore, a \(\ell_{2,p}\) -norm regularization is incorporated into the feature regression framework to enhance robustness. Extensive experiments conducted on six benchmark datasets demonstrate that our method outperforms multiple state-of-the-art models. Graph learning Non-negative matrix factorization Multi-view unsupervised feature selection Low-rank tensor learning 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-7158182","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501829087,"identity":"18d6ba59-4b66-43d7-83c3-957268106b01","order_by":0,"name":"Jianjun Jiang","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Jiang","suffix":""},{"id":501829089,"identity":"5036821d-5646-4f3a-a610-f75c5ee52c47","order_by":1,"name":"Xijiong Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFCCA0BoICEHZj4gXkuBhTGYmUC8TR8qEhtANFFaDA6eMTzwwUAifX7Y4YdAW+zkdBsIaJFsOJZwcIaBRO7G22kGQC3JxmYHCGjhZzh84DAPSMvsBJCWA4nbCGlhYzjYcPgP0GGGs9M/EKcFbAswkBPkpXOItAXslx4DCcMN0jkFBxIMiPCLwY0zxh9+/KmTl5+dvvnDhwo7OYJaGCSgKgzAtAEh5SDA3wCh5RuIUT0KRsEoGAUjEgAAIixLS/7aA00AAAAASUVORK5CYII=","orcid":"","institution":"Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Xijiong","middleName":"","lastName":"Xie","suffix":""},{"id":501829090,"identity":"e3c3e1dd-a0d6-414f-9196-137fb75daa74","order_by":2,"name":"Guoqing Chao","email":"","orcid":"","institution":"Harbin Institute of Technology at Weihai","correspondingAuthor":false,"prefix":"","firstName":"Guoqing","middleName":"","lastName":"Chao","suffix":""}],"badges":[],"createdAt":"2025-07-18 13:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7158182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7158182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90072098,"identity":"05463ef2-c1b1-4e3d-b2ee-be3d633a4554","added_by":"auto","created_at":"2025-08-28 07:09:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2293154,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7158182/v1_covered_a8933738-e46d-4f31-96d9-a65254e276f8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-view Unsupervised Feature Selection Guided by Latent Representation and Tensor Learning","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":"Graph learning, Non-negative matrix factorization, Multi-view unsupervised feature selection, Low-rank tensor learning","lastPublishedDoi":"10.21203/rs.3.rs-7158182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7158182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMulti-view unsupervised feature selection refers to the process of selecting the most representative or relevant feature subset from a dataset containing multiple views. 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