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- W4319660770 abstract "Abstract The estimation of mixture models is a key to understanding and visualizing the distribution of multivariate data. Most approaches to learning mixture models involve likelihoods and are not adapted to distributions 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 with distances between distributions. The Augmented Quantization algorithm can handle mixtures of discrete and continuous measures, with finite or infinite supports. Each step of the algorithm is supported by local minimizations of the generalized quantization error. 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." @default.
- W4319660770 created "2023-02-10" @default.
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- W4319660770 date "2023-10-10" @default.
- W4319660770 modified "2023-10-11" @default.
- W4319660770 title "Augmented quantization: a general approach to mixture models" @default.
- W4319660770 doi "https://doi.org/10.21203/rs.3.rs-3410853/v1" @default.
- W4319660770 hasPublicationYear "2023" @default.
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