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Approximation of functions over manifolds : A Moving Least-Squares approach (2021)

Sober, B., Aizenbud, Y., & Levin, D. (2021). Approximation of functions over manifolds : A Moving Least-Squares approach. Journal of Computational and Applied Mathematics, 383, Article 113140. https://doi.org/10.1016/j.cam.2020.113140

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Sober, Barak; Aizenbud, Yariv; Levin, David

Lehti tai sarja: Journal of Computational and Applied Mathematics

ISSN: 0377-0427

eISSN: 1879-1778

Julkaisuvuosi: 2021

Volyymi: 383

Artikkelinumero: 113140

Kustantaja: Elsevier BV

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

DOI: https://doi.org/10.1016/j.cam.2020.113140

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/77962

Rinnakkaistallenteen verkko-osoite (pre-print): https://arxiv.org/abs/1711.00765


We present an algorithm for approximating a function defined over a d-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require knowledge about the local geometry of the manifold or its local parameterizations. We do require, however, knowledge regarding the manifold's intrinsic dimension d. We use the Manifold Moving Least-Squares approach of Sober and Levin (2019) to reconstruct the atlas of charts and the approximation is built on top of those charts. The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold. In other words, given a new point, located near the manifold, the approximation can be evaluated directly on that point. We prove that our construction yields a smooth function, and in case of noiseless samples the approximation order is O(hm+1), where h is a local density of sample parameter (i.e., the fill distance) and m is the degree of a local polynomial approximation, used in our algorithm. In addition, the proposed algorithm has linear time complexity with respect to the ambient space's dimension. Thus, we are able to avoid the computational complexity, commonly encountered in high dimensional approximations, without having to perform non-linear dimension reduction, which inevitably introduces distortions to the geometry of the data. Additionally, we show numerically that our approach compares favorably to some well-known approaches for regression over manifolds.

YSO-asiasanat: numeerinen analyysi; approksimointi; funktiot; monistot

Vapaat asiasanat: dimension reduction; high dimensional approximation; manifold learning; moving least-squares; out-of-sample extension; regression over manifolds

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2021

JUFO-taso: 2

Viimeisin päivitys 2022-20-09 klo 15:21