ASAP - A Sub-sampling Approach for Preserving Topological Structures

Abolfazl Taghribi, Kerstin Bunte, Michele Mastropietro, Sven De Rijcke, Peter Tino

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
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Topological data analysis tools enjoy increasing popularity in a wide range of applications. However, due to computational complexity, processing large number of samples of higher dimensionality quickly becomes infeasible. We propose a novel sub-sampling strategy inspired by Coulomb’s law to decrease the number of data points in d-dimensional point clouds while preserving its Homology. The method is not only capable of reducing the memory and computation time needed for the construction of different types of simplicial complexes but also preserves the size of the voids in d-dimensions, which is crucial e.g. for astronomical applications. We demonstrate and compare the strategy in several synthetic scenarios and an astronomical particle simulation of a Jellyfish galaxy for the detection of superbubbles (supernova signatures).
Original languageEnglish
Title of host publicationProceedings of the 28th European Symposium on Artificial Neural Networks (ESANN)
EditorsM. Verleysen
PublisherCiaco -
Number of pages1
ISBN (Print)978-2-87587-074-2
Publication statusPublished - 2020
EventThe 28th European Symposium on Artificial Neural Networks: ESANN 2020 - Bruges, Belgium
Duration: 22-Apr-202024-Apr-2020


ConferenceThe 28th European Symposium on Artificial Neural Networks


  • Topological Data Analysis
  • Persistent Homology
  • Sub-sampling
  • Particle simulation
  • Supernova shells


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