Skip to Main content Skip to Navigation
Journal articles

Introduction to vector quantization and its applications for numerics*

Abstract : 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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [77 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01263773
Contributor : Serena Benassù <>
Submitted on : Monday, February 1, 2016 - 3:15:46 PM
Last modification on : Friday, March 27, 2020 - 3:56:25 AM
Document(s) archivé(s) le : Saturday, November 12, 2016 - 12:15:29 AM

File

proc144802.pdf
Publication funded by an institution

Licence


Distributed under a Creative Commons Attribution - NonCommercial 4.0 International License

Identifiers

Citation

G. Pagès. Introduction to vector quantization and its applications for numerics*. ESAIM: Proceedings and Surveys, EDP Sciences, 2015, 48 (1), pp.29-79. ⟨10.1051/proc/201448002⟩. ⟨hal-01263773⟩

Share

Metrics

Record views

604

Files downloads

672