Semi-Supervised Dimensionality Reduction Method Utilizing Pairwise Constraints and Integrating Similarity and Dissimilarity among Data

Semi-Supervised Dimensionality Reduction Method Utilizing Pairwise Constraints and Integrating Similarity and Dissimilarity among Data | 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 Article Semi-Supervised Dimensionality Reduction Method Utilizing Pairwise Constraints and Integrating Similarity and Dissimilarity among Data Zixuan Liu, Rong Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4495676/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 preprocessing stages, including data reconstruction and dimensionality reduction, play a pivotal role in influencing subsequent machine learning and data mining endeavors. While supervised dimensionality reduction typically surpasses unsupervised methods through direct label exploitation, the scarcity or high acquisition cost of labeled data in practical scenarios has fueled interest in semi-supervised alternatives. These methodologies ingeniously integrate sparse labeled and abundant unlabeled data, to aid data representation and dimensionality reduction.This study introduces a novel nonlinear semi-supervised dimensionality reduction algorithm, which employs pairwise constraints (must-link and cannot-link) and radial basis functions to obtain similarity and dissimilarity among data points. Leveraging spectral decomposition, our algorithm aims to reconstruct the initial data, thereby exposing intricate nonlinear patterns obscured within high-dimensional datasets. Compared to traditional semi-supervised algorithms, a series of experiments based on real data have verified the effectiveness of the proposed method. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Statistics 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. 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