The Mondrian Kernel
This work provides a new insight into the relationship between kernel methods and random forests, though it appears incremental as it builds on existing Mondrian process techniques.
The authors introduced the Mondrian kernel, a fast random feature approximation to the Laplace kernel that enables efficient batch and online learning with rapid kernel-width selection by reusing random features across widths.
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.