Visualizing Riemannian data with Rie-SNE

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dc.contributor.authorBergsson, Andrien_US
dc.contributor.authorHauberg, Sørenen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorNabney, Ianen_US
dc.contributor.editorPeltonen, Jaakkoen_US
dc.date.accessioned2024-05-21T08:51:08Z
dc.date.available2024-05-21T08:51:08Z
dc.date.issued2024
dc.description.abstractFaithful visualizations of data residing on manifolds must take the underlying geometry into account when producing a flat planar view of the data. In this paper, we extend the stochastic neighbor embedding (SNE) algorithm to data on general Riemannian manifolds. We replace standard Gaussian assumptions with Riemannian diffusion counterparts and propose an efficient approximation that only requires access to calculations of Riemannian distances and volumes. We demonstrate that the approach also allows for mapping data from one manifold to another, e.g. from a high-dimensional sphere to a low-dimensional one.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20241123
dc.identifier.isbn978-3-03868-256-1
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/mlvis.20241123
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/mlvis20241123
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject!
dc.titleVisualizing Riemannian data with Rie-SNEen_US
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Machine Learning Methods in Visualisation for Big Data 2024