Adaptive Methods for Center Choosing of Radial Basis Function Interpolation: A Review

Radial basis functions provide powerful meshfree method for multivariate interpolation for scattered data. But both the approximation quality and stability depend on the distribution of the center set. Many methods have been constructed to select optimal center sets for radial basis function interpolation. A review of these methods is given. Four kinds of center choosing algorithms which are thinning algorithm, greedy algorithm, arclength equipartition like algorithm and k-means clustering algorithm are introduced with some algorithmic analysis.

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