Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning

Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width 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 Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning Razieh Torkamani, Hadi Zayyani, Mehdi Korki, Farokh Marvasti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4892652/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted 12 You are reading this latest preprint version Abstract This paper proposes a robust adaptive algorithm for smooth graph signal recovery which is based on generalized correntropy. A proper cost function is defined, which takes the smoothness and generalized correntropy into account. The generalized correntropy used in this paper employs the generalized Gaussian density (GGD) function as the kernel. The proposed adaptive algorithm is derived and a kernel width learning-based version of the algorithm is suggested. The simulation results confirm the performance of the proposed algorithm for learning the kernel-width to the fixed correntropy kernel version of the algorithm. Moreover, some theoretical analyses of the proposed algorithm are provided. In this regard, firstly, the convexity analysis of the cost function is discussed. Secondly, the uniform stability of the algorithm is investigated. Thirdly, the mean convergence analysis is also added. Finally, the computational complexity analysis of the algorithm is incorporated. In addition, some synthetic and real-world experiments show the efficiency of the proposed algorithm in comparison to some other adaptive algorithms in the literature of adaptive graph signal recovery. Graph signal recovery robust kernel-width Generalized correntropy non-Gaussian noise Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2024 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 19 Aug, 2024 Reviews received at journal 19 Aug, 2024 Reviews received at journal 16 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers invited by journal 13 Aug, 2024 Editor assigned by journal 11 Aug, 2024 Submission checks completed at journal 11 Aug, 2024 First submitted to journal 10 Aug, 2024 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-4892652","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342390543,"identity":"823a57e9-c78d-44bf-812d-827c845e3ecd","order_by":0,"name":"Razieh Torkamani","email":"","orcid":"","institution":"Bu-Ali Sina University","correspondingAuthor":false,"prefix":"","firstName":"Razieh","middleName":"","lastName":"Torkamani","suffix":""},{"id":342390545,"identity":"1ecbf372-88a0-49bb-a41e-567c440c6dae","order_by":1,"name":"Hadi Zayyani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYPACCTkYi4doLcYMDMykaWFIbIBqIQzk28+YSfzcYZHedyP/AMOPGgYZ8wYCWhh7cswke89I5M68kQzkHGPgkTlAQAszQ46ZBG+bRO4GoBYG3gYGHglCDmPjf2Mm+bdNIt0AZMtfYrTwSOSYSQNtSQBpYSbKFgmJZ8XWsm0ShjPPPDY4LHNMgrAW+f7kjTffttXJ8x1PfPjwTY2NPUEtDAwcJhBFB8CICA0MDOyPP8C0jIJRMApGwSjACgA9FzWqIHkZjgAAAABJRU5ErkJggg==","orcid":"","institution":"Qom University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hadi","middleName":"","lastName":"Zayyani","suffix":""},{"id":342390547,"identity":"2b459f75-c75a-4438-88e3-d1ffbb7a4c88","order_by":2,"name":"Mehdi Korki","email":"","orcid":"","institution":"Swinburne University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Korki","suffix":""},{"id":342390548,"identity":"0cdfdb69-6fe4-412c-ad49-e5bb5ea601d9","order_by":3,"name":"Farokh Marvasti","email":"","orcid":"","institution":"Sharif University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Farokh","middleName":"","lastName":"Marvasti","suffix":""}],"badges":[],"createdAt":"2024-08-10 17:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4892652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4892652/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-024-03712-1","type":"published","date":"2024-12-05T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70964716,"identity":"51480fd0-a533-401c-93e4-1693e521ee8b","added_by":"auto","created_at":"2024-12-09 16:14:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":940795,"visible":true,"origin":"","legend":"","description":"","filename":"LATEXFILES.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4892652/v1_covered_cfce59e5-6f67-42c9-b4c2-4e2e28b71657.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning\n\n\n","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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Graph signal recovery, robust, kernel-width, Generalized correntropy, non-Gaussian noise","lastPublishedDoi":"10.21203/rs.3.rs-4892652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4892652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper proposes a robust adaptive algorithm for smooth graph signal recovery which is based on generalized correntropy. 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