Manifold Modelling with Minimum Spanning Trees
dc.contributor.author | Bot, Daniël M. | en_US |
dc.contributor.author | Huo, Peiyang | en_US |
dc.contributor.author | Arleo, Alessio | en_US |
dc.contributor.author | Paulovich, Fernando | en_US |
dc.contributor.author | Aerts, Jan | en_US |
dc.contributor.editor | Kucher, Kostiantyn | en_US |
dc.contributor.editor | Diehl, Alexandra | en_US |
dc.contributor.editor | Gillmann, Christina | en_US |
dc.date.accessioned | 2024-05-21T08:45:39Z | |
dc.date.available | 2024-05-21T08:45:39Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Recent 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.sectionheaders | Posters | |
dc.description.seriesinformation | EuroVis 2024 - Posters | |
dc.identifier.doi | 10.2312/evp.20241088 | |
dc.identifier.isbn | 978-3-03868-258-5 | |
dc.identifier.pages | 3 pages | |
dc.identifier.uri | https://doi.org/10.2312/evp.20241088 | |
dc.identifier.uri | https://diglib.eg.org/handle/10.2312/evp20241088 | |
dc.publisher | The Eurographics Association | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | CCS Concepts: Computing methodologies → Dimensionality reduction and manifold learning | |
dc.subject | Computing methodologies → Dimensionality reduction and manifold learning | |
dc.title | Manifold Modelling with Minimum Spanning Trees | en_US |