Non-Parametric Manifold Learning

Dena Marie Asta

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arXiv · 2107.08089

Non-Parametric Manifold Learning

Dena Marie Asta

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Abstract

We introduce an estimator for distances in a compact Riemannian manifold based on graph Laplacian estimates of the Laplace-Beltrami operator. We upper bound the error in the estimate of manifold distances, or more precisely an estimate of a spectrally truncated variant of manifold distance of interest in non-commutative geometry (cf. [Connes and Suijelekom, 2020]), in terms of spectral errors in the graph Laplacian estimates and, implicitly, several geometric properties of the manifold. A consequence is a proof of consistency for (untruncated) manifold distances. The estimator resembles, and in fact its convergence properties are derived from, a special case of the Kontorovic dual reformulation of Wasserstein distance known as Connes' Distance Formula.

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Dena Marie Asta. 2021-07-16. Non-Parametric Manifold Learning. https://arxiv.org/abs/2107.08089

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