Learning multivariate functions with low-dimensional structures using polynomial bases

DOI10.1016/j.cam.2021.113821zbMath1476.65024arXiv1912.03195OpenAlexW3202735065MaRDI QIDQ2667104

Daniel Potts, M. Schmischke

Publication date: 24 November 2021

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.03195

zbMATH Keywords

Chebyshev polynomialsorthogonal polynomialsANOVA decompositionhigh-dimensional approximation

Mathematics Subject Classification ID

Multidimensional problems (41A63) Algorithms for approximation of functions (65D15) Approximate quadratures (41A55) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (5)

ANOVAapprox ⋮ Grouped Transformations and Regularization in High-Dimensional Explainable ANOVA Approximation ⋮ Error guarantees for least squares approximation with noisy samples in domain adaptation ⋮ Learning in high-dimensional feature spaces using ANOVA-based fast matrix-vector multiplication ⋮ Interpretable Approximation of High-Dimensional Data

Uses Software

• LSQR
• NFFT
• GitHub
• QMC4PDE
• CRAIG

Cites Work

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