An Information Theoretic Perspective to Kernel K-Means

In this paper, we provide an information theoretic perspective to kernel K-means. We show that kernel K-means corresponds to maximizing an integrated squared error divergence measure between Parzen window estimated cluster probability density functions. Equivalently, this corresponds to a Bayes-like clustering rule in the input space, taking into account the Renyi entropies of the clusters.