Python implementation of the Vectorial Kernel Orthogonal Greedy Algorithm.
The algorithm is implemented as a scikit-learn Estimator, and it can be used via the fit and predict methods.
The best way to start using the algorithm is having a look at the demo notebook, which can also be executed online on Binder:
If you use this code in your work, please cite the paper
G. Santin and B. Haasdonk, Kernel Methods for Surrogate Modeling, ArXiv preprint 1907.10556 (2019).
@TechReport{SaHa2019,
Author = {Santin, Gabriele and Haasdonk, Bernard},
Title = {Kernel Methods for Surrogate Modeling},
Year = {2019},
Number = {1907.10556},
Type = {ArXiv},
Url = {https://arxiv.org/abs/1907.10556}
}
For further details on the algorithm and its implementation, please refer to the following papers:
M. Pazouki and R. Schaback, Bases for kernel-based spaces, J. Comput. Appl. Math., 236, 575-588 (2011).
D. Wirtz and B. Haasdonk, A Vectorial Kernel Orthogonal Greedy Algorithm, Dolomites Res. Notes Approx., 6, 83-100 (2013).
G. Santin, D. Wittwar, B. Haasdonk, Greedy regularized kernel interpolation, ArXiv preprint 1807.09575 (2018).
T. Wenzel, G. Santin, B. Haasdonk, A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution, ArXiv preprint 1911.04352 (2019).
The original Matlab version of this software is maintained here.