2022 - T3 - WS2 - Geometry, Topology and Statistics in Data Sciences

Collection 2022 - T3 - WS2 - Geometry, Topology and Statistics in Data Sciences

Organizer(s) Aamari, Eddie ; Aaron, Catherine ; Chazal, Frédéric ; Fischer, Aurélie ; Hoffmann, Marc ; Le Brigant, Alice ; Levrard, Clément ; Michel, Bertrand

Date(s) 10/10/2022 - 14/10/2022

linked URL https://indico.math.cnrs.fr/event/7546/

12 15

Approximating data with a union of ellipsoids and clustering

By Claire Brécheteau

I will introduce a surrogate for the distance function to the support of a distribution, which sublevel sets are unions of balls or of ellipsoids. I will expose different results, including rates of convergence for the approximadomtion of these surrogates with their empirical versions, built from pointclouds. I will explain how to use such estimators to cluster data with a geometric structure. The results have been published in the papers [1,2], and are still in progress. [1] C. Brécheteau. Robust anisotropic power-functions-based filtrations for clustering. In 36th International Symposium on Computational Geometry (SoCG 2020), vol. 164, 2020. [2] C. Brécheteau, C. Levrard. A k-points-based distance for robust geometric inference. Bernoulli, 26(4), 2020.

Information about the video

Date of publication 05/04/2024
Institution IHP
Language English
Format MP4

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