Neighbour Embeddings: Beyond Visualisation

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dc.contributor.authorLambert, Pierreen_US
dc.contributor.authorCouplet, Edouarden_US
dc.contributor.authorVerleysen, Michelen_US
dc.contributor.authorLee, John Aldoen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorNabney, Ianen_US
dc.contributor.editorPeltonen, Jaakkoen_US
dc.date.accessioned2025-05-26T07:12:44Z
dc.date.available2025-05-26T07:12:44Z
dc.date.issued2025
dc.description.abstractMachine learning (ML) has brought powerful tools to the visualisation community, particularly through neighbour embeddings (NE). This family of algorithms enables the intuitive visualisation of high dimensional datasets, by representing these in 2- or 3-dimensional spaces. This paper argues that as NE algorithms have progressed and diversified within the visualisation domain, they have matured into powerful yet often simple methods whose potential remains largely underutilised in broader machine learning contexts. This argument is illustrated by showing through two use cases, clustering and data preprocessing before a supervised task, how NE can contribute meaningfully with little additional algorithmic effort.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20251156
dc.identifier.isbn978-3-03868-288-2
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/mlvis.20251156
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/mlvis20251156
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleNeighbour Embeddings: Beyond Visualisationen_US
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Machine Learning Methods in Visualisation for Big Data 2025