Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets

DOI10.1007/s11634-021-00466-3OpenAlexW3203718269MaRDI QIDQ2673358

Robin Fuchs, Cinzia Viroli, Denys Pommeret

Publication date: 9 June 2022

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.06661

zbMATH Keywords

MCEM algorithmgeneralized linear latent variable modelbinary and count datadeep Gaussian mixture modelordinal and categorical datatwo-heads architecture

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

Variational inference and sparsity in high-dimensional deep Gaussian mixture models

Uses Software

• Silhouettes
• UCI-ml
• DiffSharp
• Autograd

Cites Work

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• Deep Gaussian mixture models
• Modelling high-dimensional data by mixtures of factor analyzers
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Finite mixture models and model-based clustering
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Estimating the dimension of a model
• A factor mixture model for analyzing heterogeneity and cognitive structure of dementia
• Generalized latent trait models
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Finite mixture models
• Multiple Factor Analysis by Example Using R

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