Parsimonious Gaussian mixture models

Authors:
Paul David Mcnicholas;Thomas Brendan Murphy
Affiliations:
Department of Mathematics and Statistics, University of Guelph, Guelph, Canada N1G 2W1;School of Mathematical Sciences, University College Dublin, Dublin, Ireland 4
Venue:
Statistics and Computing
Year:
2008

Citing 6
Cited 22

Latent variable models and factors analysis

Latent variable models and factors analysis
Mixtures of probabilistic principal component analyzers

Neural Computation
Numerical Recipes in C: The Art of Scientific Computing

Numerical Recipes in C: The Art of Scientific Computing
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling high-dimensional data by mixtures of factor analyzers

Computational Statistics & Data Analysis
Enhanced Model-Based Clustering, Density Estimation,and Discriminant Analysis Software: MCLUST

Journal of Classification

Penalized factor mixture analysis for variable selection in clustered data

Computational Statistics & Data Analysis
Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models

Computational Statistics & Data Analysis
Robust mixture modeling using multivariate skew t distributions

Statistics and Computing
Model-based classification via mixtures of multivariate t-distributions

Computational Statistics & Data Analysis
Dimension reduction for model-based clustering

Statistics and Computing
Extending mixtures of multivariate t-factor analyzers

Statistics and Computing
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

Statistics and Computing
Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

Statistics and Computing
Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution

Statistics and Computing
Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm

Journal of Multivariate Analysis
Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

Statistics and Computing
The infinite Student's t-factor mixture analyzer for robust clustering and classification

Pattern Recognition
Clustering and classification via cluster-weighted factor analyzers

Advances in Data Analysis and Classification
Using evolutionary algorithms for model-based clustering

Pattern Recognition Letters
Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA

Advances in Data Analysis and Classification
Dimension reduction for model-based clustering via mixtures of multivariate $$t$$t-distributions

Advances in Data Analysis and Classification
Using conditional independence for parsimonious model-based Gaussian clustering

Statistics and Computing
Model-based clustering of high-dimensional data: A review

Computational Statistics & Data Analysis
Model-based clustering via linear cluster-weighted models

Computational Statistics & Data Analysis
Parsimonious skew mixture models for model-based clustering and classification

Computational Statistics & Data Analysis
A LASSO-penalized BIC for mixture model selection

Advances in Data Analysis and Classification
Mixtures of biased sentiment analysers

Advances in Data Analysis and Classification

Quantified Score

Hi-index	0.00

Visualization

Abstract

Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases.In particular, a class of eight parsimonious Gaussian mixture models which are based on the mixtures of factor analyzers model are introduced and the maximum likelihood estimates for the parameters in these models are found using an AECM algorithm. The class of models includes parsimonious models that have not previously been developed.These models are applied to the analysis of chemical and physical properties of Italian wines and the chemical properties of coffee; the models are shown to give excellent clustering performance.