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Electronic Journal of Statistics
Article . 2024 . Peer-reviewed
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zbMATH Open
Article . 2024
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https://dx.doi.org/10.48550/ar...
Article . 2021
License: CC BY
Data sources: Datacite
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Non-parametric manifold learning

Authors: Asta, Dena Marie;

Non-parametric manifold learning

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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Keywords

FOS: Computer and information sciences, consistency, Connes' distance formula, Mathematics - Statistics Theory, Machine Learning (stat.ML), Statistics Theory (math.ST), Laplace-Beltrami operator, Statistics - Machine Learning, manifold learning, graph Laplacian, FOS: Mathematics, Wasserstein distance, Nonparametric estimation, Statistics on manifolds

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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