NANANov 30, 2016

A rescaled method for RBF approximation

arXiv:1611.1003413 citationsh-index: 26
AI Analysis

For researchers in kernel-based interpolation, this work offers theoretical grounding for a previously heuristic method, but it is incremental as it reframes rather than introduces new capabilities.

The paper provides a theoretical analysis of a rescaled interpolation method for RBF approximation, showing it is equivalent to standard kernel interpolation with a rescaled kernel, enabling error and stability analysis.

In the recent paper [8], a new method to compute stable kernel-based interpolants has been presented. This \textit{rescaled interpolation} method combines the standard kernel interpolation with a properly defined rescaling operation, which smooths the oscillations of the interpolant. Although promising, this procedure lacks a systematic theoretical investigation. Through our analysis, this novel method can be understood as standard kernel interpolation by means of a properly rescaled kernel. This point of view allow us to consider its error and stability properties.

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