首页|Learning multivariate functions with low-dimensional structures using polynomial bases
Learning multivariate functions with low-dimensional structures using polynomial bases
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NSTL
Elsevier
In this paper we propose a method for the approximation of high-dimensional functions over finite intervals with respect to complete orthonormal systems of polynomials. An important tool for this is the multivariate classical analysis of variance (ANOVA) decomposition. For functions with a low-dimensional structure, i.e., a low superposition dimension, we are able to achieve a reconstruction from scattered data and simultaneously understand relationships between different variables. (C) 2021 Elsevier B.V. All rights reserved.
NSTL
Elsevier
