An Introduction to Variational Methods for Graphical Models
Machine Learning
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
The Variational Bayes Method in Signal Processing (Signals and Communication Technology)
The Variational Bayes Method in Signal Processing (Signals and Communication Technology)
Variational Bayesian mixture model on a subspace of exponential family distributions
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum likelihood estimation of mixtures of factor analyzers
Computational Statistics & Data Analysis
The infinite Student's t-mixture for robust modeling
Signal Processing
Variational learning for Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast ML Estimation for the Mixture of Factor Analyzers via an ECM Algorithm
IEEE Transactions on Neural Networks
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Recently, a representative approach, named mixtures of common factor analyzers (MCFA), was proposed for clustering high-dimensional observed data. Existing model-parameter estimation methods for this approach is based on the maximum likelihood criterion and performed by the expectation-maximization algorithm. In this paper, we consider the MCFA from a Bayesian perspective and propose the Bayesian mixtures of common factor analyzers (BMCFA) model, which replaces the deterministic model parameters in the MCFA by stochastic variables. Then we present a variational inference algorithm for this BMCFA model. Moreover, the proposed BMCFA model and the associated variational inference algorithm are used for clustering the high-dimensional synthetic data, the wine data from the UCI machine learning repository and the gene expression data. Experimental results illustrate that the BMCFA has good generalization capacities, automatically determining the appropriate number of clusters from high-dimensional observed data.