The Mondrian Kernel
Abstract
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a Mondrian process [Roy and Teh, 2009], and we highlight the connection to Mondrian forests [Lakshminarayanan et al., 2014], where trees are also sampled via a Mondrian process, but fit independently. This link provides a new insight into the relationship between kernel methods and random forests.
Cite
Text
Balog et al. "The Mondrian Kernel." Conference on Uncertainty in Artificial Intelligence, 2016.Markdown
[Balog et al. "The Mondrian Kernel." Conference on Uncertainty in Artificial Intelligence, 2016.](https://mlanthology.org/uai/2016/balog2016uai-mondrian/)BibTeX
@inproceedings{balog2016uai-mondrian,
title = {{The Mondrian Kernel}},
author = {Balog, Matej and Lakshminarayanan, Balaji and Ghahramani, Zoubin and Roy, Daniel M. and Teh, Yee Whye},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2016},
url = {https://mlanthology.org/uai/2016/balog2016uai-mondrian/}
}