2014/02/18 by Alex Davies, Zoubin Ghahramani, Davies, Alex +1 · 2 citations
Computer Science · #Anomaly Detection Techniques and Applications #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1402.4293
openalex publication_date 2014/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present Random Partition Kernels, a new class of kernels derived by\ndemonstrating a natural connection between random partitions of objects and\nkernels between those objects. We show how the construction can be used to\ncreate kernels from methods that would not normally be viewed as random\npartitions, such as Random Forest. To demonstrate the potential of this method,\nwe propose two new kernels, the Random Forest Kernel and the Fast Cluster\nKernel, and show that these kernels consistently outperform standard kernels on\nproblems involving real-world datasets. Finally, we show how the form of these\nkernels lend themselves to a natural approximation that is appropriate for\ncertain big data problems, allowing O(N) inference in methods such as\nGaussian Processes, Support Vector Machines and Kernel PCA.\n