A Clustering-based Multiple Kernel Learning Algorithm for Multi-Class Classification

A Clustering-based Multiple Kernel Learning Algorithm for Multi-Class Classification | 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 A Clustering-based Multiple Kernel Learning Algorithm for Multi-Class Classification Zhang xiaofeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4543845/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 Multiple kernel learning algorithms typically optimize kernel alignment, structural risk minimization, and Bayesian functions. However, they have limitations, including inapplicability to multi-class classification, high time complexity, and no analytic solution. Analyzing clustering and classification similarities, we propose a novel clustering-based multiple kernel learning algorithm for multi-class classification (CBMKL). This algorithm transforms input space to high-dimension feature space using multiple kernel mapping functions. It estimates base kernel function weights and constructs the decision function using clustering objectives. This CBMKL algorithm has several advantages. 1) It handles multi-class problems directly. 2) This algorithm has an analytical solution, avoiding approximate solutions from sampling methods. 3) It also has polynomial time complexity. Experiments on two datasets illustrate these advantages. multiple kernel learning multi-class classification kernel clustering 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-4543845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317200246,"identity":"a531d1e4-c54a-4c03-83ad-1d0bf1e97482","order_by":0,"name":"Zhang xiaofeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFAC5gYJBgYJBvv2xsYHQC4PH2EtjBAtBjyHmw1AWtiI1MLAYCCR3gZmENTCD3TP7YoaizxzhsS2yq85djJsDMwPH93Ao0XizMFmyzPHJIotGw623Zbdlgx0GJuxcQ4eLQYSiW2SDWwSiQ0HG9tuS25jBmrhYZMmrOUfUMthxrZiyW31RGppbJNI3HCMsY3x47bDhLWA/dLYJ5E4s4exWZpx23EeNmYCfuFvbz54s+FbXWK//POHH39uq7bnZ29++BifFhTAzAMmiVUOAow/SFE9CkbBKBgFIwYAAGrJSE6jFibqAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangsu Administration Institute","correspondingAuthor":true,"prefix":"","firstName":"Zhang","middleName":"","lastName":"xiaofeng","suffix":""}],"badges":[],"createdAt":"2024-06-07 06:12:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4543845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4543845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64725884,"identity":"3fc87d42-fd72-4699-bf10-6446db2d9f5b","added_by":"auto","created_at":"2024-09-18 05:39:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":251994,"visible":true,"origin":"","legend":"","description":"","filename":"MultipleKernelLearningAlgorithm20240620.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4543845/v1_covered_5191f609-3578-4285-a367-c527c8f6251b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Clustering-based Multiple Kernel Learning Algorithm for Multi-Class Classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"multiple kernel learning, multi-class classification, kernel clustering","lastPublishedDoi":"10.21203/rs.3.rs-4543845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4543845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultiple kernel learning algorithms typically optimize kernel alignment, structural risk minimization, and Bayesian functions. 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