Manifold Modelling with Minimum Spanning Trees
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Computing methodologies → Dimensionality reduction and manifold learning
@inproceedings{10.2312:evp.20241088,
booktitle = {EuroVis 2024 - Posters},
editor = {Kucher, Kostiantyn and Diehl, Alexandra and Gillmann, Christina},
title = {{Manifold Modelling with Minimum Spanning Trees}},
author = {Bot, Daniël M. and Huo, Peiyang and Arleo, Alessio and Paulovich, Fernando and Aerts, Jan},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-258-5},
DOI = {10.2312/evp.20241088}
}