AISTATS 2019poster15 citations
Reducing training time by efficient localized kernel regression
Abstract
We study generalization properties of kernel regularized least squares regression based on a partitioning approach. We show that optimal rates of convergence are preserved if the number of local sets grows sufficiently slowly with the sample size. Moreover, the partitioning approach can be efficiently combined with local Nyström subsampling, improving computational cost twofold.
BibTeX
@InProceedings{pmlr-v89-muecke19a,
title = {Reducing training time by efficient localized kernel regression},
author = {M\"{u}ecke, Nicole},
booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
pages = {2603--2610},
year = {2019},
editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
volume = {89},
series = {Proceedings of Machine Learning Research},
month = {16--18 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v89/muecke19a/muecke19a.pdf},
url = {https://proceedings.mlr.press/v89/muecke19a.html},
abstract = {We study generalization properties of kernel regularized least squares regression based on a partitioning approach. We show that optimal rates of convergence are preserved if the number of local sets grows sufficiently slowly with the sample size. Moreover, the partitioning approach can be efficiently combined with local Nyström subsampling, improving computational cost twofold.}
}