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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