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Conference Papers Year : 2020

Robust anisotropic power-functions-based filtrations for clustering


We consider robust power-distance functions that approximate the distance function to a compact set, from a noisy sample. We pay particular interest to robust power-distance functions that are anisotropic, in the sense that their sublevel sets are unions of ellipsoids, and not necessarily unions of balls. Using persistence homology on such power-distance functions provides robust clustering schemes. We investigate such clustering schemes and compare the different procedures on synthetic and real datasets. In particular, we enhance the good performance of the anisotropic method for some cases for which classical methods fail.
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Comment : Implementation of the algorithm. Sources for the experiments. (with the R software

Dates and versions

hal-02397100 , version 1 (06-12-2019)
hal-02397100 , version 2 (25-03-2020)


  • HAL Id : hal-02397100 , version 2


Claire Brécheteau. Robust anisotropic power-functions-based filtrations for clustering. Symposium on Computational Geometry, Jun 2020, Zurich, Switzerland. ⟨hal-02397100v2⟩
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