Mixtures of probabilistic principal component analyzers
Neural Computation
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Enhanced Model-Based Clustering, Density Estimation,and Discriminant Analysis Software: MCLUST
Journal of Classification
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Parsimonious Gaussian mixture models
Statistics and Computing
Computational Statistics & Data Analysis
Dimension reduction for model-based clustering
Statistics and Computing
Model-based classification via mixtures of multivariate t-distributions
Computational Statistics & Data Analysis
Dimension reduction for model-based clustering via mixtures of multivariate $$t$$t-distributions
Advances in Data Analysis and Classification
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
A multivariate linear regression analysis using finite mixtures of t distributions
Computational Statistics & Data Analysis
Model-based clustering via linear cluster-weighted models
Computational Statistics & Data Analysis
Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition
Computational Statistics & Data Analysis
Parsimonious skew mixture models for model-based clustering and classification
Computational Statistics & Data Analysis
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Model-based clustering typically involves the development of a family of mixture models and the imposition of these models upon data. The best member of the family is then chosen using some criterion and the associated parameter estimates lead to predicted group memberships, or clusterings. This paper describes the extension of the mixtures of multivariate t-factor analyzers model to include constraints on the degrees of freedom, the factor loadings, and the error variance matrices. The result is a family of six mixture models, including parsimonious models. Parameter estimates for this family of models are derived using an alternating expectation-conditional maximization algorithm and convergence is determined based on Aitken's acceleration. Model selection is carried out using the Bayesian information criterion (BIC) and the integrated completed likelihood (ICL). This novel family of mixture models is then applied to simulated and real data where clustering performance meets or exceeds that of established model-based clustering methods. The simulation studies include a comparison of the BIC and the ICL as model selection techniques for this novel family of models. Application to simulated data with larger dimensionality is also explored.