An adaptive residual sub-sampling algorithm for kernel interpolation based on maximum likelihood estimations
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Publication:6394773
DOI10.1016/J.CAM.2022.114658arXiv2203.14089WikidataQ113878682 ScholiaQ113878682MaRDI QIDQ6394773
Roberto Cavoretto, Alessandra De Rossi
Publication date: 26 March 2022
Abstract: In this paper we propose an enhanced version of the residual sub-sampling method (RSM) in [9] for adaptive interpolation by radial basis functions (RBFs). More precisely, we introduce in the context of sub-sampling methods a maximum profile likelihood estimation (MPLE) criterion for the optimal selection of the RBF shape parameter. This choice is completely automatic, provides highly reliable and accurate results for any RBFs, and, unlike the original RSM, guarantees that the RBF interpolant exists uniquely. The efficacy of this new method, called MPLE-RSM, is tested by numerical experiments on some 1D and 2D benchmark target functions.
Numerical interpolation (65D05) Algorithms for approximation of functions (65D15) Numerical radial basis function approximation (65D12)
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