Geodesic
Introduction to vector quantization and its applications for numerics
Gilles Pagès1
1 Laboratoire de Probabilités et Modèles aléatoires, UMR 7599, UPMC, case 188, 4, pl. Jussieu, F-75252 Paris Cedex 5, France.
ESAIM. Proceedings, Tome 48 (2015), pp. 29-79 Cet article a éte moissonné depuis la source EDP Sciences

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We present an introductory survey to optimal vector quantization and its first applications to Numerical Probability and, to a lesser extent to Information Theory and Data Mining. Both theoretical results on the quantization rate of a random vector taking values in ℝd (equipped with the canonical Euclidean norm) and the learning procedures that allow to design optimal quantizers (CLVQ and Lloyd’s procedures) are presented. We also introduce and investigate the more recent notion of greedy quantization which may be seen as a sequential optimal quantization. A rate optimal result is established. A brief comparison with Quasi-Monte Carlo method is also carried out.
DOI : 10.1051/proc/201448002

Gilles Pagès 1

1 Laboratoire de Probabilités et Modèles aléatoires, UMR 7599, UPMC, case 188, 4, pl. Jussieu, F-75252 Paris Cedex 5, France. 
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Gilles Pagès. Introduction to vector quantization and its applications for numerics. ESAIM. Proceedings, Tome 48 (2015), pp. 29-79. doi: 10.1051/proc/201448002

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