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Augmented quantization : a general approach to mixture models

Abstract

Quantization methods classically provide a discrete representation of a continuous set. This type of representation is relevant when the objective is the visualisation of weighted prototype elements representative of a continuous phenomenon. Nevertheless, more complex descriptions may be investigated. In this sense, mixture models identify subpopulations in a sample, arising from different distributions. The Gaussian mixture model is particularly popular and relies
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Dates and versions

hal-03975978 , version 1 (06-02-2023)

Identifiers

  • HAL Id : hal-03975978 , version 1

Cite

Charlie Sire, Didier Rullière, Rodolphe Le Riche, Jérémy Rohmer, Yann Richet, et al.. Augmented quantization : a general approach to mixture models. MASCOT-NUM2023, UQ@Paris-Saclay, Apr 2023, LE CROISIC, France. ⟨hal-03975978⟩
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