Super-samples from kernel herding
Yutian Chen, Max Welling, Alex J. Smola · 2010
We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting “kernel herding ” algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rateO(1/T) which is much faster than the usual O(1 / √ T) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions. 1