IEEE GlobalSIP 2016
Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear 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 Kmeans 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 Nystr¨om based approximations.
Pourkamali Anaraki, Farhad and Becker, Stephen, "A Randomized Approach to Efficient Kernel Clustering" (2016). Applied Mathematics Faculty Contributions. 16.