Mixtures of common factor analyzers for high-dimensional data with missing information

DOI10.1016/j.jmva.2013.02.003zbMath1277.62162OpenAlexW2004116879MaRDI QIDQ391593

Wan-Lun Wang

Publication date: 10 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2013.02.003

zbMATH Keywords

clusteringdiscriminant analysisdimension reductionECM algorithmcommon factor loadingsmixture factor analyzers

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Flexible clustering via extended mixtures of common \(t\)-factor analyzers ⋮ Automated learning of factor analysis with complete and incomplete data ⋮ Mixtures of common \(t\)-factor analyzers for modeling high-dimensional data with missing values ⋮ Clustering with the multivariate normal inverse Gaussian distribution ⋮ Extending mixtures of factor models using the restricted multivariate skew-normal distribution ⋮ Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution ⋮ Mixtures of restricted skew-\(t\) factor analyzers with common factor loadings

Uses Software

• mclust
• R
• AS 136
• impute

Cites Work

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