Kernelized Renyi distance for subset selection and similarity scoring

Renyi entropy refers to a generalized class of entropies that have beenused in several applications. In this work, we derive a non-parametric
distance between distributions based on the quadratic Renyi entropy. The
distributions are estimated via Parzen density estimates. The quadratic
complexity of the distance evaluation is mitigated with GPU-based
parallelization. This results in an efficiently evaluated non-parametric
entropic distance - the kernelized Renyi distance or the KRD. We adapt
the KRD into a similarity measure and show its application to speaker
recognition. We further extend KRD to measure dissimilarities between
distributions and illustrate its applications to statistical subset
selection and dictionary learning for object recognition and pose
estimation.