Sparse radial basis function approximation with spatially variable shape parameters
作者：
Highlights：
• A greedy algorithm is proposed for function approximation using a parametrized dictionary of radial basis functions.
• Significant reductions in the memory requirement and training cost are achieved using an incremental QR factorization scheme.
• The proposed algorithm provides exceptional modeling flexibility via the use of spatially variable shape parameters.
• All the model parameters are estimated efficiently in a single run without using expensive cross-validation procedures.
摘要
•A greedy algorithm is proposed for function approximation using a parametrized dictionary of radial basis functions.•Significant reductions in the memory requirement and training cost are achieved using an incremental QR factorization scheme.•The proposed algorithm provides exceptional modeling flexibility via the use of spatially variable shape parameters.•All the model parameters are estimated efficiently in a single run without using expensive cross-validation procedures.
论文关键词：Function approximation,Parameterized dictionary learning,Radial basis functions,Greedy algorithm,Shape parameter tuning,Surrogate modeling
论文评审过程：Received 27 May 2016, Accepted 1 February 2018, Available online 8 March 2018, Version of Record 8 March 2018.
论文官网地址：https://doi.org/10.1016/j.amc.2018.02.001
