The Mondrian Kernel
The Mondrian Kernel
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.
Accepted for presentation at the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)
- University of Cambridge
- University College London United Kingdom
- University of Oxford United Kingdom
- University of Cambridge United Kingdom
- University of Oxford
FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML), stat.ML
FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML), stat.ML
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