Efficient Computation of Distribution Kernels and Distances

摘要

Similarity and dissimilarity measures such as kernels and distances are key components of classification and clustering algorithms. We propose an efficient algorithm for computation of kernel and distance functions between two probability distributions. The complexity of the proposed algorithm is insensitive to the dimension of the input space and therefore especially suitable for high dimensional distributions.