Open Access
February 2019 Nonasymptotic rates for manifold, tangent space and curvature estimation
Eddie Aamari, Clément Levrard
Ann. Statist. 47(1): 177-204 (February 2019). DOI: 10.1214/18-AOS1685

Abstract

Given a noisy sample from a submanifold $M\subset\mathbb{R}^{D}$, we derive optimal rates for the estimation of tangent spaces $T_{X}M$, the second fundamental form $\mathit{II}_{X}^{M}$ and the submanifold $M$. After motivating their study, we introduce a quantitative class of $\mathcal{C}^{k}$-submanifolds in analogy with Hölder classes. The proposed estimators are based on local polynomials and allow to deal simultaneously with the three problems at stake. Minimax lower bounds are derived using a conditional version of Assouad’s lemma when the base point $X$ is random.

Citation

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Eddie Aamari. Clément Levrard. "Nonasymptotic rates for manifold, tangent space and curvature estimation." Ann. Statist. 47 (1) 177 - 204, February 2019. https://doi.org/10.1214/18-AOS1685

Information

Received: 1 April 2017; Revised: 1 October 2017; Published: February 2019
First available in Project Euclid: 30 November 2018

zbMATH: 07036199
MathSciNet: MR3909931
Digital Object Identifier: 10.1214/18-AOS1685

Subjects:
Primary: 62C20 , 62G05

Keywords: Geometric inference , manifold learning , minimax

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.47 • No. 1 • February 2019
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