Robust Dual Space Factorization for Unsupervised Feature Selection (RDSF-UFS)

Robust Dual Space Factorization for Unsupervised Feature Selection (RDSF-UFS) | 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 Dual Space Factorization for Unsupervised Feature Selection (RDSF-UFS) Masoud Karimzadeh, Parham Moradi, Abdulbaghi Ghaderzadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6388045/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 20 You are reading this latest preprint version Abstract Unsupervised feature selection is critical in high-dimensional data analysis, as it identifies the most informative features without relying on label information. In this paper, we propose a novel Robust Dual Space Factorization for Unsupervised Feature Selection (RDSF-UFS) framework based on Symmetric Nonnegative Matrix Factorization (SNMF) and Nonnegative Matrix Tri-Factorization (NMTF). The proposed method explores sample and feature affinity matrices to capture intrinsic data structures in a dual latent space. Specifically, we introduce a robust objective function incorporating the Frobenius and í µí°¿ 2,1 norms to enhance resistance to noise and outliers. We further impose an orthogonality constraint on the latent factor matrices to promote sparsity and improve clustering performance. An efficient optimization algorithm is derived to iteratively update the latent factors by solving the associated gradient-based equations. We evaluated the performance of RDSFUFS on eight benchmark datasets, including biological microarray, face image, speech signal, and digit image datasets. Experimental results demonstrate that RDSFUFS outperforms state-of-the-art feature selection methods in terms of Normalized Mutual Information (NMI) and Accuracy (ACC), highlighting its ability to uncover meaningful feature patterns and improve clustering performance. Unsupervised Feature Selection High-Dimensional Data Analysis Robust Dual Space Factorization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviews received at journal 01 Jun, 2025 Reviews received at journal 29 May, 2025 Reviews received at journal 25 May, 2025 Reviews received at journal 21 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviews received at journal 14 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 13 May, 2025 Editor assigned by journal 09 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 06 Apr, 2025 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-6388045","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456641246,"identity":"bc4b1d15-422a-46fe-b155-d37541597e53","order_by":0,"name":"Masoud Karimzadeh","email":"","orcid":"","institution":"Department of Computer Engineering, Sa. 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