Unsupervised metric learning via K-ISOMAP for high-dimensional data clustering

Unsupervised metric learning via K-ISOMAP for high-dimensional data clustering | 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 metric learning via K-ISOMAP for high-dimensional data clustering Rodrigo de P. Mendes, Gustavo H. Chavari, Alexandre L. M. Levada This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4168452/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 Clustering does not assume the knowledge of the class labels, defining an unsupervised learning paradigm relevant for many pattern recognition and machine learning problems. One of the limitations of clustering is that most algorithms rely heavily in a distance function used to compute a dissimilarity measure between the samples. The usual choice is the Euclidean distance, which is known to have a poor discriminant power in high-dimensional spaces and also is quite sensitive to the presence of outliers. In this paper, we propose to investigate how unsupervised metric learning via the curvature-based ISOMAP algorithm (K-ISOMAP) can influence the clustering performance by means of quantitative evaluation metrics. The computation of the local curvature in the dimensionality reduction process allows the incorporation of an adaptive and intrinsic distance metric function into clustering, making it more aware of the geometry of the underlying data manifold. Computational experiments with real-world datasets indicate that the use of K-ISOMAP prior to a clustering algorithm may produce superior Rand, Calinski-Harabasz and Fowlkes-Mallows indices, in comparison to raw and regular ISOMAP data, suggesting that learning a suitable metric can be a relevant pre-processing step before clustering. Clustering unsupervised metric learning ISOMAP graph-based learning 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-4168452","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284374521,"identity":"58798403-a378-46f4-b100-f4b1819e1b28","order_by":0,"name":"Rodrigo de P. 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