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Preprints, Working Papers, ... Year : 2023

Principal subbundles for dimension reduction

Abstract

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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hal-04156036 , version 1 (07-07-2023)

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Morten Akhøj, James Benn, Erlend Grong, Stefan Sommer, Xavier Pennec. Principal subbundles for dimension reduction. 2023. ⟨hal-04156036⟩
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