ANOVA-Boosting for Random Fourier Features: An Interpretable Approach to High-Dimensional Function Approximation

The authors propose two algorithms that utilize the ANOVA decomposition to learn low-order functions with few variable interactions, enabling reliable identification of important input variables and their interactions. This approach improves the interpretability of existing random Fourier feature models.