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Found 8 papers, 3 papers with code
ANOVA-boosting for Random Fourier Features
We propose two algorithms for boosting random Fourier feature models for approximating high-dimensional functions.
Fast and interpretable Support Vector Classification based on the truncated ANOVA decomposition
In small dimensional settings the Fast Fourier Transform (FFT) and related methods are a powerful tool in order to deal with the considered basis functions.
Interpretable transformed ANOVA approximation on the example of the prevention of forest fires
We demonstrate the applicability of this procedure on the well-known forest fires data set from the UCI machine learning repository.
Interpretable Approximation of High-Dimensional Data
The advantage of this method is the interpretability of the approximation, i. e., the ability to rank the importance of the attribute interactions or the variable couplings.
Grouped Transformations and Regularization in High-Dimensional Explainable ANOVA Approximation
From there we propose a fast matrix-vector multiplication, the grouped Fourier transform, for high-dimensional grouped index sets.
Numerical Analysis Numerical Analysis 65T, 42B05
Uniform error estimates for nonequispaced fast Fourier transforms
We present novel error estimates for NFFT with compactly supported, continuous window functions and derive rules for convenient choice from the parameters involved in NFFT.
Numerical Analysis Numerical Analysis 65T50, 94A12, 42A10
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.
Numerical Analysis Numerical Analysis
NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks
The graph Laplacian is a standard tool in data science, machine learning, and image processing.
