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Preprints, Working Papers, ... Year : 2023

Augmented quantization: a general approach to mixture models


The investigation of mixture models is a key to understand and visualize the distribution of multivariate data. Most mixture models approaches are based on likelihoods, and are not adapted to distribution with finite support or without a well-defined density function. This study proposes the Augmented Quantization method, which is a reformulation of the classical quantization problem but which uses the p-Wasserstein distance. This metric can be computed in very general distribution spaces, in particular with varying supports. The clustering interpretation of quantization is revisited in a more general framework. The performance of Augmented Quantization is first demonstrated through analytical toy problems. Subsequently, it is applied to a practical case study involving river flooding, wherein mixtures of Dirac and Uniform distributions are built in the input space, enabling the identification of the most influential variables.

Dates and versions

hal-04209768 , version 1 (18-09-2023)



Charlie Sire, Rodolphe Le Riche, Didier Rullière, Jérémy Rohmer, Yann Richet, et al.. Augmented quantization: a general approach to mixture models. 2023. ⟨hal-04209768⟩
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