Mixtures of common \(t\)-factor analyzers for modeling high-dimensional data with missing values

DOI10.1016/j.csda.2014.10.007OpenAlexW2067144247MaRDI QIDQ1623795

Wan-Lun Wang

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.10.007

zbMATH Keywords

visualizationclusteringdimension reductionmissing datacommon factor loadingsECME algorithm

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)

Related Items (8)

The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers ⋮ Identifying connected components in Gaussian finite mixture models for clustering ⋮ Location and scale mixtures of Gaussians with flexible tail behaviour: properties, inference and application to multivariate clustering ⋮ Automated learning of \(t\) factor analysis models with complete and incomplete data ⋮ Robust factored principal component analysis for matrix-valued outlier accommodation and detection ⋮ 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

• R
• UCI-ml
• AS 136

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Modelling high-dimensional data by mixtures of factor analyzers
• A note on variational Bayesian factor analysis
• Dimensionally reduced model-based clustering through mixtures of factor mixture analyzers
• Mixtures of common factor analyzers for high-dimensional data with missing information
• Extending mixtures of factor models using the restricted multivariate skew-normal distribution
• Extension of the mixture of factor analyzers model to incorporate the multivariate \(t\)-distribution
• Estimating the dimension of a model
• The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family
• Learning from incomplete data via parameterized \(t\) mixture models through eigenvalue decomposition
• Mixtures of skew-\(t\) factor analyzers
• Maximum likelihood estimation of mixtures of factor analyzers
• An efficient ECM algorithm for maximum likelihood estimation in mixtures of \(t\)-factor analyzers
• Bayesian Analysis of Mixtures of Factor Analyzers
• Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
• Maximum likelihood estimation via the ECM algorithm: A general framework
• The EM Algorithm and Extensions, 2E
• Model-Based Gaussian and Non-Gaussian Clustering
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
• Multivariate T-Distributions and Their Applications
• Breast Cancer Diagnosis and Prognosis Via Linear Programming
• Heteroscedastic factor mixture analysis
• Extending mixtures of multivariate \(t\)-factor analyzers

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