| Title: |
Manifold Modelling with Minimum Spanning Trees |
| Authors: |
Bot, Daniël M.; Huo, Peiyang; Arleo, Alessio; Paulovich, Fernando V.; Aerts, Jan |
| Contributors: |
Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina |
| Source: |
Bot, D M, Huo, P, Arleo, A, Paulovich, F V & Aerts, J 2024, Manifold Modelling with Minimum Spanning Trees. in K Kucher, A Diehl & C Gillmann (eds), EuroVis 2024 - Posters. Eurographics Association, 26th Eurographics Conference on Visualization, EuroVis 2024, Odense, Denmark, 27/05/24. https://doi.org/10.2312/evp.20241088 |
| Publisher Information: |
Eurographics Association |
| Publication Year: |
2024 |
| Description: |
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. |
| Document Type: |
conference object |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/isbn/978-3-03868-258-5 |
| DOI: |
10.2312/evp.20241088 |
| Availability: |
https://research.tue.nl/en/publications/e5b3c944-8376-45f7-9792-dfbf549515b6; https://doi.org/10.2312/evp.20241088; https://pure.tue.nl/ws/files/360542835/14_evp20241088_1_.pdf |
| Rights: |
info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.E9998479 |
| Database: |
BASE |