Reviewer #2: ''Why didn't you use UMAP?''

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dc.contributor.authorHoorn, Diede P. M. van deren_US
dc.contributor.authorArleo, Alessioen_US
dc.contributor.authorPaulovich, Fernando V.en_US
dc.contributor.editorDiehl, Alexandraen_US
dc.contributor.editorKucher, Kostiantynen_US
dc.contributor.editorMédoc, Nicolasen_US
dc.date.accessioned2025-05-26T06:55:04Z
dc.date.available2025-05-26T06:55:04Z
dc.date.issued2025
dc.description.abstractt-SNE and UMAP are both popular Dimensionality Reduction (DR) techniques. Over recent years, UMAP has gained popularity, but there has been some debate on the difference between the two, and whether a preference for UMAP is justified. We apply a recently defined framework to gain new insights by analyzing these two techniques in two phases: how they model the relationships in the high-dimensional space (relationship phase) and how they create the embedding (mapping phase). Our findings suggest that the main difference lies in the UMAP mapping phase, and not in how the relationships are modeled.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2025 - Posters
dc.identifier.doi10.2312/evp.20251132
dc.identifier.isbn978-3-03868-286-8
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20251132
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/evp20251132
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visualization; Computing methodologies → Dimensionality reduction and manifold learning
dc.subjectHuman centered computing → Visualization
dc.subjectComputing methodologies → Dimensionality reduction and manifold learning
dc.titleReviewer #2: ''Why didn't you use UMAP?''en_US
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