2022 - T3 - WS1 - Non-linear and high dimensional inference

Collection 2022 - T3 - WS1 - Non-linear and high dimensional inference

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

Date(s) 03/10/2022 - 07/10/2022

linked URL https://indico.math.cnrs.fr/event/7545/

18 21

Understanding the geometry of high-dimensional data through the reach

By Clément Bérenfeld

In high-dimensional statistics, and more particularly in manifold learning, the reach is a ubiquitous regularity parameter that encompasses the well-behavior of the support of the underlying probability measure. Enforcing a reach constraint is, in most geometric inference tasks, a necessity, which raises the question of the estimability of this parameter.We will try to understand how the reach relates to many other important geometric invariants and propose and estimation strategy that relies on estimating the intrinsic metric of the data. (Joint work with Eddie Aamari and Clément Levrard)

Information about the video

Date of publication 05/04/2024
Institution IHP
Licence CC BY-NC-ND
Language English
Format MP4

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