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/

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Topologically penalized regression on manifolds

By Wolfgang Polonik

We study a regression problem on a compact manifold. In order to take advantage of the underlying geometry and topology of the data, we propose to perform the regression task on the basis of eigenfunctions of the Laplace-Beltrami operator of the manifold that are regularized with topological penalties. We will discuss the approach and the penalties, provide some supporting theory and illustrate the performance of the methodology on some data sets, illustrating the relevance of our approach in the case where the target function is "topologically smooth”. This is joint work with O. Hacquard, K. Balasubramanian, G. Blanchard and C. Levrard

Information about the video

Date of recording 04/10/2022
Date of publication 26/04/2024
Institution IHP
Language English
Audience Researchers, Graduate Students
Director(s) Alexandre Duplessis, Marco Perez
Format MP4
Venue Amphitheater Hermite, IHP

Citation data

DOI 10.57987/IHP.2022.T3.WS1.006
Cite this video Polonik, Wolfgang (04/10/2022). Topologically penalized regression on manifolds. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS1.006
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS1.006

Domain(s)

Machine Learning

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