2016/06/16 by Matej Balog, Balog, Matej, Balaji Lakshminarayanan +7 · 2 citations
Mathematics · #FOS: Computer and information sciences #Machine Learning (stat.ML) #stat.ML
paper · pdf · doi:10.48550/arxiv.1606.05241
Accepted for presentation at the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)
arxiv created 2016/06/16 · arxiv updated 2016/06/17
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.