ANOVA-boosting for Random Fourier Features

arXiv:2404.03050v1 Announce Type: new
Abstract: 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 …

abstract algorithms analysis anova arxiv boosting cs.lg cs.na feature features fourier functions generalized index interactions learn low math.na random set stat.ml type variables variance