ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis
Machine Learning
2012-12-10 v2
Abstract
Given a reproducing kernel Hilbert space H of real-valued functions and a suitable measure mu over the source space D (subset of R), we decompose H as the sum of a subspace of centered functions for mu and its orthogonal in H. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the e ffect of each (group of) variable(s) and computing sensitivity indices without recursivity.
Keywords
Cite
@article{arxiv.1106.3571,
title = {ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis},
author = {Nicolas Durrande and David Ginsbourger and Olivier Roustant and Laurent Carraro},
journal= {arXiv preprint arXiv:1106.3571},
year = {2012}
}