Manifold Modelling with Minimum Spanning Trees

dc.contributor.authorBot, Daniël M.en_US
dc.contributor.authorHuo, Peiyangen_US
dc.contributor.authorArleo, Alessioen_US
dc.contributor.authorPaulovich, Fernandoen_US
dc.contributor.authorAerts, Janen_US
dc.contributor.editorKucher, Kostiantynen_US
dc.contributor.editorDiehl, Alexandraen_US
dc.contributor.editorGillmann, Christinaen_US
dc.date.accessioned2024-05-21T08:45:39Z
dc.date.available2024-05-21T08:45:39Z
dc.date.issued2024
dc.description.abstractRecent dimensionality reduction algorithms operate on a manifold assumption and expect data to be uniformly sampled from that underlying manifold. While some algorithms attempt to be robust for non-uniform sampling, their reliance on k-nearest neighbours to approximate manifolds limits how well they can span sampling gaps without introducing shortcuts. We present a minimum-spanning-tree-based manifold approximation approach that overcomes this problem and demonstrate it crosses sampling-gaps without introducing shortcuts while creating networks with few edges. A python package implementing our algorithm is available at https://github.com/vda-lab/multi_mst.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2024 - Posters
dc.identifier.doi10.2312/evp.20241088
dc.identifier.isbn978-3-03868-258-5
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20241088
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/evp20241088
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Dimensionality reduction and manifold learning
dc.subjectComputing methodologies → Dimensionality reduction and manifold learning
dc.titleManifold Modelling with Minimum Spanning Treesen_US
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