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Kernelizing Vector Quantization Algorithms

Abstract : The kernel trick is a well known approach allowing to implicitly cast a linear method into a nonlinear one by replacing any dot product by a kernel function. However few vector quantization algorithms have been kernelized. Indeed, they usually imply to compute linear transformations (e.g. moving prototypes), what is not easily kernelizable. This paper introduces the Kernel-based Vector Quantization (KVQ) method which allows working in an approximation of the feature space, and thus kernelizing any Vector Quantization (VQ) algorithm.
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Contributor : Sébastien van Luchene Connect in order to contact the contributor
Submitted on : Friday, December 4, 2009 - 12:00:29 PM
Last modification on : Monday, December 14, 2020 - 2:10:02 PM
Long-term archiving on: : Thursday, June 17, 2010 - 6:02:01 PM


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  • HAL Id : hal-00429892, version 1



Matthieu Geist, Olivier Pietquin, Gabriel Fricout. Kernelizing Vector Quantization Algorithms. ESANN'2009, Apr 2009, Bruges, Belgium. pp.541-546. ⟨hal-00429892⟩



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