subbundle
Principal subbundles for dimension reduction
Akhøj, Morten, Benn, James, Grong, Erlend, Sommer, Stefan, Pennec, Xavier
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
2307.03128
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Learning in High Dimensional Spaces (0.41)