Kernel-Based Methods for Hypothesis Testing: A Unified View
Zaïd Harchaoui, Francis Bach, Olivier Cappé, Éric Moulines · IEEE Signal Processing Magazine · 2013
Kernel-based methods provide a rich and elegant framework for developing nonparametric detection procedures for signal processing. Several recently proposed procedures can be simply described using basic concepts of reproducing kernel Hilbert space (RKHS) embeddings of probability distributions, mainly mean elements and covariance operators. We propose a unified view of these tools and draw relationships with information divergences between distributions.