Subsampling Methods for Persistent Homology

Abstract : Persistent homology is a multiscale method for analyzing the shape of sets and functions from point cloud data arising from an unknown distribution supported on those sets. When the size of the sample is large, direct computation of the persistent homology is prohibitive due to the combinatorial nature of the existing algorithms. We propose to compute the persistent homology of several subsamples of the data and then combine the resulting estimates. We study the risk of two estimators and we prove that the subsampling approach carries stable topological information while achieving a great reduction in computational complexity.
Type de document :
Communication dans un congrès
International Conference on Machine Learning (ICML 2015), Jul 2015, Lille, France
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Contributeur : Frédéric Chazal <>
Soumis le : mercredi 8 octobre 2014 - 19:19:22
Dernière modification le : jeudi 20 juillet 2017 - 09:27:17


  • HAL Id : hal-01073073, version 1
  • ARXIV : 1406.1901


Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, et al.. Subsampling Methods for Persistent Homology. International Conference on Machine Learning (ICML 2015), Jul 2015, Lille, France. <hal-01073073>



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