Extrinsic and Intrinsic Operator Estimations for Manifold Learning | Vidéo | Carmin.tv

Extrinsic and Intrinsic Operator Estimations for Manifold Learning

Apparaît dans la collection : 2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

I will discuss the radial-basis function pointwise and weak formulations for approximating Laplacians on functions and vector fields based on randomly sampled point cloud data, whose spectral properties are relevant to manifold learning. For the pointwise formulation, I will demonstrate the importance of the novel local tangent estimation that accounts for the curvature, which crucially improves the quality of the operator estimation. I will report the spectral theoretical convergence results of these formulations and their strengths/weaknesses in practice. Supporting numerical examples, involving the spectral estimation of the Laplace-Beltrami operator and various vector Laplacians such as the Bochner, Hodge, and Lichnerowicz Laplacians will be demonstrated with appropriate comparisons to the standard graph-based approaches. If time permits, I will discuss an alternative graph-based formulation for identifying manifolds with boundary.

Informations sur la vidéo

Date de captation 04/10/2022
Date de publication 04/10/2022
Institut IHP
Langue Anglais
Audience Chercheurs, Doctorants
Réalisateur(s) Alexandre Duplessis, Marco Perez
Format MP4
Lieu Amphitheater Hermite, IHP

Données de citation

DOI 10.57987/IHP.2022.T3.WS1.004
Citer cette vidéo Harlim John (04/10/2022). Extrinsic and Intrinsic Operator Estimations for Manifold Learning. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS1.004
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS1.004

Domaine(s)

Analyse numérique

Bibliographie

J. Harlim, S.W. Jiang, J. Wilson Peoples / Radial basis approximation of tensor fields on manifolds: From operator estimation to manifold learning arXIv:2208.08369
J. Wilson Peoples, J. Harlim / Spectral convergence of symmetrized graph Laplacian on manifolds with boundary arXiv:2110.06988

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