University of California, Santa Cruz
The authors present random partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. They show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, they propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, they show how the form of these kernels lends themselves to a natural approximation that is appropriate for certain big data problems, allowing O(N) inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.