Principal subbundles for dimension reduction
Résumé
In this paper we demonstrate how sub-Riemannian geometry can be used for manifold learning and surface reconstruction by combining local linear approximations of a point cloud to obtain lower dimensional bundles. Local approximations obtained by local PCAs are collected into a rank $k$ tangent subbundle on $\mathbb{R}^d$, $k
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https://inria.hal.science/hal-04156036
Soumis le : vendredi 7 juillet 2023-16:29:46
Dernière modification le : mercredi 6 novembre 2024-10:54:02
Citer
Morten Akhøj, James Benn, Erlend Grong, Stefan Sommer, Xavier Pennec. Principal subbundles for dimension reduction. 2023. ⟨hal-04156036⟩
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