Kernel-Based Methods for Hypothesis Testing: A Unified View - Inria - Institut national de recherche en sciences et technologies du numérique
Journal Articles IEEE Signal Processing Magazine Year : 2013

Kernel-Based Methods for Hypothesis Testing: A Unified View

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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Dates and versions

hal-00841978 , version 1 (05-07-2013)

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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, 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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