Kernel-Based Methods for Hypothesis Testing: A Unified View

Zaid Harchaoui 1 Francis Bach 2, 3 Olivier Cappé 4 Eric Moulines 4
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : 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 embeddings of probability distributions, namely mean elements and covariance operators. We propose a unified view of these tools, and draw relationships with information divergences between distributions.
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Zaid Harchaoui, Francis Bach, Olivier Cappé, Eric Moulines. Kernel-Based Methods for Hypothesis Testing: A Unified View. IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2013, Special Issue on Advances in Kernel-Based Learning for Signal Processing, 30 (4), pp.87-97. ⟨10.1109/MSP.2013.2253631⟩. ⟨hal-00841978⟩

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