Generalized Similarity Distance based Canonical CorrelationAnalysis (GSDCCA) for Multiview Data Representation

Generalized Similarity Distance based Canonical CorrelationAnalysis (GSDCCA) for Multiview Data Representation | 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 Generalized Similarity Distance based Canonical CorrelationAnalysis (GSDCCA) for Multiview Data Representation Surendra Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4709826/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 Data representation has a significant impact on how well the standard multi-viewmachine learning algorithms perform. Existing data representation methods commonly construct a latent subspace such that transformed projections of sample datafrom multiple views are maximized in the common subspace. Different views aregenerally heterogeneous and may contain either similar or complementary information. To be able to remove common information from multiple views such that thedata contains unique information, an extension of the Canonical Correlation Analysis is presented in this paper as the Generalized Similarity Distance based CanonicalCorrelation Analysis (GSDCCA). The proposed approach exploits complementaryand coherent information between the views and intrinsic structural informationwithin the view to build a comprehensive latent space for multi-view machine learning algorithms. The Bhattacharya similarity distance approach is used to exploit thecomplementary and coherent information between the views to project the sampledata onto a common subspace for correlation analysis. Moreover, local neighbouring information is exploit as to obtain the view that preserves the local structureinformation of sample data while performing global dimensionality reduction. Experiments on real-world datasets and synthetic datasets demonstrate that the GSDCCAapproach can build a vast latent space that consistently captures the complementaryand coherent information between views and intrinsic structure information withinthe view. The performance of the proposed approach was compared to other relatedapproaches, and the results show that it is an effective and promising approachfor real-world applications and superior to other state-of-the-art approaches. It isalso observed that the method is highly suitable where views contain lots of similarinformation. Generalized canonical correlation analysis Similarity distance Feature extraction Subspace learning Complementary analysis Multiview data representation 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-4709826","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332397645,"identity":"6ee81fd7-1c03-4073-9139-72fc34b41a93","order_by":0,"name":"Surendra Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYPACGyBmbICwmYnRcIAhDaSlsYEULYcZkKwhAOT715g9/lBz3l7e/3D7wx8MdvIM7LwH8GoxuPHG3ODAsduJG28kNjbzMCQbNjDzJeDXInHGTOIA2+0EwxmMjc1AjyQwMPMY4HfYDJCWf+fsDfsPNjb+YKgnrIXhfI+ZxMG2A4zzGRIbG3gYDhPWYnCDrdzgbF9y4gaJxMbZPAbHDdsIOqz/8LYHFd/s7OX7jz/4+KOiWp6f/wwBh0kksEGsOwAmGRjY8KsHAv4DEDXyDQSVjoJRMApGwUgFAAbNROkLuPFyAAAAAElFTkSuQmCC","orcid":"","institution":"Rajiv Gandhi Technical University","correspondingAuthor":true,"prefix":"","firstName":"Surendra","middleName":"","lastName":"Gupta","suffix":""}],"badges":[],"createdAt":"2024-07-09 07:07:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4709826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4709826/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75281129,"identity":"4f900e27-04c3-41a3-b6f1-7fab8d58c926","added_by":"auto","created_at":"2025-02-03 02:38:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":595968,"visible":true,"origin":"","legend":"","description":"","filename":"GSDCCA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4709826/v1_covered_19cbac45-67cd-4477-a515-33bd0c885f02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generalized Similarity Distance based Canonical CorrelationAnalysis (GSDCCA) for Multiview Data Representation","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":"Generalized canonical correlation analysis, Similarity distance, Feature extraction, Subspace learning, Complementary analysis, Multiview data representation","lastPublishedDoi":"10.21203/rs.3.rs-4709826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4709826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eData representation has a significant impact on how well the standard multi-viewmachine learning algorithms perform. 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