The Mondrian Kernel
Machine Learning
2016-06-17 v1
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.
Keywords
Cite
@article{arxiv.1606.05241,
title = {The Mondrian Kernel},
author = {Matej Balog and Balaji Lakshminarayanan and Zoubin Ghahramani and Daniel M. Roy and Yee Whye Teh},
journal= {arXiv preprint arXiv:1606.05241},
year = {2016}
}
Comments
Accepted for presentation at the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)