@InProceedings{Supelec488,

author = {Matthieu Geist and Olivier Pietquin and Gabriel Fricout},

title = {Kernelizing Vector Quantization Algorithms},

year = {2009},

booktitle = {Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN 09)},

pages = {541-546},

month = {April},

editor = {Michel Verleysen},

address = {Bruges (Belgium)},

url = {http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-49.pdf},

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.}

}