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Learning multivariate functions with low-dimensional structures using polynomial bases (1912.03195v4)
Published 6 Dec 2019 in math.NA and cs.NA
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.