Signed random Fourier features for fast density estimation with indefinite kernels
Kernel density estimation (KDE) is one of the most fundamental statistical estimators of density functions. Its direct implementation on a dataset of $N$ points incurs an $\mathcal{O}(N^{2})$ computational cost, which is prohibitive for large-scale datasets. Kernel approximation techniques can be applied to bring the computational cost down to $\mathcal{O}(N)$. The random Fourier features (RFF) technique, based on sampling from the spectral density of the kernel function, has become popular to speed up kernel estimators for machine learning applications. Unfortunately, it is restricted to positive definite kernels, while the majority of kernel functions popular in KDE, such as the parabol...
| Authors | Xie Wang, Nicolas Langrené, Wen Chen |
|---|---|
| Primary category | stat.CO |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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