Local RBF-based penalized least-squares approximation on the sphere with noisy scattered data-Reference-Cited by-同舟云学术

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Local RBF-based penalized least-squares approximation on the sphere with noisy scattered data

Published:2021-01 Issue: Volume:382 Page:113061
ISSN:0377-0427
Container-title:Journal of Computational and Applied Mathematics
language:en
Short-container-title:Journal of Computational and Applied Mathematics

Author:

Hesse Kerstin^ORCID,Sloan Ian H.^ORCID,Womersley Robert S.

Funder

National Science Foundation

Australian Research Council

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Mathematics

Reference35 articles.

1. Handbook of Geomathematics,2015

2. Handbook of Mathematical Geodesy (Functional Analytic and Potential Theoretic Methods),2018

3. Spherical Sampling;Freeden,2018

4. Fitting scattered data on sphere-like surfaces using spherical splines;Alfeld;J. Comput. Appl. Math.,1996

5. Data fitting by penalized least squares;von Golitschek,1990

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data;SIAM Journal on Imaging Sciences;2024-05-20

2. Lasso Hyperinterpolation Over General Regions;SIAM Journal on Scientific Computing;2021-01

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