Using conditional independence for parsimonious model-based Gaussian clustering

DOI10.1007/s11222-012-9336-6zbMath1322.62167OpenAlexW2040898759MaRDI QIDQ746302

Gabriele Soffritti, Giuliano Galimberti

Publication date: 16 October 2015

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

Full work available at URL: https://doi.org/10.1007/s11222-012-9336-6

zbMATH Keywords

EM algorithmBayesian information criterionspectral decompositionconditional independenceGaussian mixture model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (6)

Multivariate response and parsimony for Gaussian cluster-weighted models ⋮ Model-based clustering of high-dimensional data: a review ⋮ Comparing classical criteria for selecting intra-class correlated features in Multimix ⋮ Modelling the role of variables in model-based cluster analysis ⋮ Model-based clustering with sparse covariance matrices ⋮ Gaussian mixture model with an extended ultrametric covariance structure

Uses Software

• mclust
• R
• UCI-ml
• Mixmod
• PGMM
• EMMIX

Cites Work

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