Quadrature-Based Features for Kernel Approximation
Abstract
We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a unifying approach that reinterprets the previous random features methods and extends to better estimates of the kernel approximation. We derive the convergence behavior and conduct an extensive empirical study that supports our hypothesis.
Cite
Text
Munkhoeva et al. "Quadrature-Based Features for Kernel Approximation." Neural Information Processing Systems, 2018.Markdown
[Munkhoeva et al. "Quadrature-Based Features for Kernel Approximation." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/munkhoeva2018neurips-quadraturebased/)BibTeX
@inproceedings{munkhoeva2018neurips-quadraturebased,
title = {{Quadrature-Based Features for Kernel Approximation}},
author = {Munkhoeva, Marina and Kapushev, Yermek and Burnaev, Evgeny and Oseledets, Ivan},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {9147-9156},
url = {https://mlanthology.org/neurips/2018/munkhoeva2018neurips-quadraturebased/}
}