Journal of Computational and Applied Mathematics2022,Vol.40319.DOI:10.1016/j.cam.2021.113821

Learning multivariate functions with low-dimensional structures using polynomial bases

Potts, D. Schmischke, M.
Journal of Computational and Applied Mathematics2022,Vol.40319.DOI:10.1016/j.cam.2021.113821

Learning multivariate functions with low-dimensional structures using polynomial bases

Potts, D. 1Schmischke, M.1
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作者信息

  • 1. Tech Univ Chemnitz
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Abstract

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.

Key words

ANOVA decomposition/High-dimensional approximation/Chebyshev polynomials/Orthogonal polynomials/DECOMPOSITION/INTEGRATION/ALGORITHMS/EQUATIONS/MACHINE

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出版年

2022
Journal of Computational and Applied Mathematics

Journal of Computational and Applied Mathematics

EISCI
ISSN:0377-0427
被引量1
参考文献量36
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