ANOVA-boosting for random Fourier features

We propose two algorithms for boosting random Fourier feature models for approximating high-dimensional functions. These methods utilize the classical and generalized analysis of variance (ANOVA) decomposition to learn low-order functions, where there are few interactions between the variables. Our...

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Bibliographic Details
Published inApplied and computational harmonic analysis Vol. 79; p. 101789
Main Authors Potts, Daniel, Weidensager, Laura
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.10.2025
Subjects
ANOVA
Global sensitivity analysis
High-dimensional approximation
Random Fourier features
Global sensitivity analysis
High-dimensional approximation
Random Fourier features
ANOVA
Online AccessGet full text
ISSN1063-5203
DOI10.1016/j.acha.2025.101789

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Summary:We propose two algorithms for boosting random Fourier feature models for approximating high-dimensional functions. These methods utilize the classical and generalized analysis of variance (ANOVA) decomposition to learn low-order functions, where there are few interactions between the variables. Our algorithms are able to find an index set of important input variables and variable interactions reliably. Furthermore, we generalize already existing random Fourier feature models to an ANOVA setting, where terms of different order can be used. Our algorithms have the advantage of being interpretable, meaning that the influence of every input variable is known in the learned model, even for dependent input variables. We provide theoretical as well as numerical results that our algorithms perform well for sensitivity analysis. The ANOVA-boosting step reduces the approximation error of existing methods significantly.
ISSN:1063-5203
DOI:10.1016/j.acha.2025.101789