Maximum likelihood estimation of mixtures of factor analyzers

Authors:
Angela Montanari;Cinzia Viroli
Affiliations:
-;-
Venue:
Computational Statistics & Data Analysis
Year:
2011

Citing 3
Cited 3

Modelling high-dimensional data by mixtures of factor analyzers

Computational Statistics & Data Analysis
Bayesian Analysis of Mixtures of Factor Analyzers

Neural Computation
The EM algorithm for the extended finite mixture of the factor analyzers model

Computational Statistics & Data Analysis

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

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

Journal of Multivariate Analysis
Bayesian mixtures of common factor analyzers: Model, variational inference, and applications

Signal Processing

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Abstract

Mixtures of factor analyzers have been receiving wide interest in statistics as a tool for performing clustering and dimension reduction simultaneously. In this model it is assumed that, within each component, the data are generated according to a factor model. Therefore, the number of parameters on which the covariance matrices depend is reduced. Several estimation methods have been proposed for this model, both in the classical and in the Bayesian framework. However, so far, a direct maximum likelihood procedure has not been developed. This direct estimation problem, which simultaneously allows one to derive the information matrix for the mixtures of factor analyzers, is solved. The effectiveness of the proposed procedure is shown on a simulation study and on a toy example.