A Randomized Approach to Efficient Kernel Clustering

ABSTRACT Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit nonlinear representation of the data. A dominant concern is the memory requirement since memory scales as the square of the number of data points. We provide a new analysis of a class of approximate kernel methods that have more modest memory requirements, and propose a specific one-pass randomized kernel approximation followed by standard K-means on the transformed data. The analysis and experiments suggest the method is accurate, while requiring drastically less memory than standard kernel K-means and significantly less memory than Nyström based approximations. Index Terms-- Kernel methods, Unsupervised learning, Lowrank approximation, Randomized algorithm 1. INTRODUCTION Kernel-based approaches are popular methods for supervised and unsupervised learning [1].