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

DOI10.1007/S11222-022-10132-ZzbMath1496.62014arXiv2105.01496OpenAlexW3157572283WikidataQ114223421 ScholiaQ114223421MaRDI QIDQ2080343

Nadja Klein, Lucas Kock, David J. Nott

Publication date: 7 October 2022

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.01496

zbMATH Keywords

variational approximationnatural gradienthigh-dimensional clusteringhorseshoe priormixtures of factor analyzersdeep clustering

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15)

Uses Software

• UCI-ml
• Scikit
• PyTorch
• ADVI

Cites Work

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• Model-based clustering based on sparse finite Gaussian mixtures
• Deep Gaussian mixture models
• Asymptotic Behaviour of the Posterior Distribution in Overfitted Mixture Models
• Modelling high-dimensional data by mixtures of factor analyzers
• Variational approximations in Bayesian model selection for finite mixture distributions
• Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets
• Sparse Bayesian infinite factor models
• Latent Variable Models and Factor Analysis
• The horseshoe estimator for sparse signals
• High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

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