Julia package ANOVAapprox: Learning multivariate functions with low-dimensional structures using polynomial bases. 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.
Keywords for this software
References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Potts, D.; Schmischke, M.: Learning multivariate functions with low-dimensional structures using polynomial bases (2022)
- Choi, Bosu; Iwen, Mark; Volkmer, Toni: Sparse harmonic transforms. II: Best (s)-term approximation guarantees for bounded orthonormal product bases in sublinear-time (2021)
- Potts, Daniel; Schmischke, Michael: Approximation of high-dimensional periodic functions with Fourier-based methods (2021)