Random sampling high dimensional model representation Gaussian process regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method

DOI10.1016/J.CPC.2021.108220zbMath1528.65012arXiv2012.02704OpenAlexW3212467745MaRDI QIDQ6156985

Owen Ren, Manabu Ihara, Sergei Manzhos, Dmitry Voytsekhovsky, Mohamed Ali Boussaidi

Publication date: 19 June 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.02704

zbMATH Keywords

machine learningGaussian process regressionhigh dimensional model representationmultivariate functiondata imputation

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Statistical sampling theory and related topics (62D99) Multidimensional problems (41A63)

Cites Work

• General foundations of high-dimensional model representations
• Efficient implementation of high dimensional model representations
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