NeurIPS 2018spotlight70 citations
Quadrature-based features for kernel approximation
Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
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.
BibTeX
@inproceedings{NEURIPS2018_6e923226,
author = {Munkhoeva, Marina and Kapushev, Yermek and Burnaev, Evgeny and Oseledets, Ivan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Quadrature-based features for kernel approximation},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/6e923226e43cd6fac7cfe1e13ad000ac-Paper.pdf},
volume = {31},
year = {2018}
}