Unsupervised feature selection based on the hidden knowledge of the Two-Dimensional Principal Component Analysis feature extraction method

Unsupervised feature selection based on the hidden knowledge of the Two-Dimensional Principal Component Analysis feature extraction method | 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 Unsupervised feature selection based on the hidden knowledge of the Two-Dimensional Principal Component Analysis feature extraction method Firoozeh Beiranvand, Vahid Mehrdad, Mohammad Bagher Dowlatshahi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4298823/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 In this paper, we proposed a new matrix-based feature selection method that used the hidden knowledge in the orthogonal features obtained from the two-dimensional principal component analysis feature extraction method with transfer learning to perform highly accurate unsupervised feature selection. We briefly named it the UFS2DPCA algorithm. In general, features can be classified as redundant, irrelevant, and relevant. Correlation is another concept of redundancy and perfectly correlated features are redundant. Accordingly, we first use the 2DPCA approach to directly extract the uncorrelated and orthogonal features from the 2-D image dataset. Uncorrelated and orthogonal features are among the best features. Next, we compute the correlation similarity between the main and extracted features. Finally, we make a weighted bipartite graph using two sets of features and the similarities between them, then we select the best features of the primary using the fast LAPJV algorithm. We evaluate the performance of the proposed UFS2DPCA algorithm on four well-known image datasets using K- Nearest Neighbor classifier. Results of comparative experiments between the proposed UFS2DPCA algorithm and eight state-of-the-art unsupervised feature selection algorithms show that the UFS2DPCA method outperforms other methods. Two-Dimensional Principal Component Analysis Transfer Learning Weighted Bipartite Graph Matching LAPJV algorithm Augmenting Path 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. 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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-4298823","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294542808,"identity":"98f98e54-d7f2-44bb-82e1-3d54ae9e88ce","order_by":0,"name":"Firoozeh Beiranvand","email":"","orcid":"","institution":"Lorestan University","correspondingAuthor":false,"prefix":"","firstName":"Firoozeh","middleName":"","lastName":"Beiranvand","suffix":""},{"id":294542809,"identity":"b2fd1e5f-84f8-497e-9406-227ed72262ca","order_by":1,"name":"Vahid Mehrdad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACgwM8DAw8DMwMDOzNDWARNkJaLOFaeA4SqcUerkUisYE4h5kd7z324U2NNYPBzYdtEgw1dgx80gcIaDlzLnnmnGPpDAa3E4FajiUzsPElENByI8eYmYftMIPkbJAWtgMMbDwEHGZw/w1Qyz+glpkHgVr+EaPlBo8xM2/bYQZ+CcY2ICJGy5kcY8a5fek8/DyJzRaJfck8hLUcP2PM8OabtRwb++GDNz58s5OT7yGgBQYgRifAGKNgFIyCUTAKKAMAYl85biGRvU0AAAAASUVORK5CYII=","orcid":"","institution":"Lorestan University","correspondingAuthor":true,"prefix":"","firstName":"Vahid","middleName":"","lastName":"Mehrdad","suffix":""},{"id":294542810,"identity":"a28dab8a-7e0c-4e74-86e4-d80f24d61539","order_by":2,"name":"Mohammad Bagher Dowlatshahi","email":"","orcid":"","institution":"Lorestan University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Bagher","lastName":"Dowlatshahi","suffix":""}],"badges":[],"createdAt":"2024-04-20 21:54:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4298823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4298823/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55955206,"identity":"78d2de22-025e-413b-8eba-98e05dba2dd2","added_by":"auto","created_at":"2024-05-06 19:55:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1875691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4298823/v1_covered_988b2a89-0e6c-41fa-8553-e0148ab73b33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unsupervised feature selection based on the hidden knowledge of the Two-Dimensional Principal Component Analysis feature extraction method","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":"Two-Dimensional Principal Component Analysis, Transfer Learning, Weighted Bipartite Graph Matching, LAPJV algorithm, Augmenting Path","lastPublishedDoi":"10.21203/rs.3.rs-4298823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4298823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we proposed a new matrix-based feature selection method that used the hidden knowledge in the orthogonal features obtained from the two-dimensional principal component analysis feature extraction method with transfer learning to perform highly accurate unsupervised feature selection. 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