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(The Eurographics Association, 2024) Bot, Daniël M.; Huo, Peiyang; Arleo, Alessio; Paulovich, Fernando; Aerts, Jan; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
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.