An Introduction to Variational Methods for Graphical Models
Machine Learning
Robust mixture modelling using the t distribution
Statistics and Computing
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
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
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionally Reduced Model-Based Clustering Through Mixtures of Factor Mixture Analyzers
Journal of Classification
Maximum likelihood estimation of mixtures of factor analyzers
Computational Statistics & Data Analysis
Extending mixtures of multivariate t-factor analyzers
Statistics and Computing
The infinite Student's t-mixture for robust modeling
Signal Processing
Signal Modeling and Classification Using a Robust Latent Space Model Based on Distributions
IEEE Transactions on Signal Processing
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Recently, the Student's t-factor mixture analyzer (tFMA) has been proposed. Compared with the mixture of Student's t-factor analyzers (MtFA), the tFMA has better performance when processing high-dimensional data. Moreover, the factors estimated by the tFMA can be visualized in a low-dimensional latent space, which is not shared by the MtFA. However, as the tFMA belongs to finite mixtures and the related parameter estimation method is based on the maximum likelihood criterion, it could not automatically determine the appropriate model complexity according to the observed data, leading to overfitting. In this paper, we propose an infinite Student's t-factor mixture analyzer (itFMA) to handle this issue. The itFMA is based on the nonparametric Bayesian statistics which assumes infinite number of mixing components in advance, and automatically determines the proper number of components after observing the high-dimensional data. Moreover, we derive an efficient variational inference algorithm for the itFMA. The proposed itFMA and the related variational inference algorithm are used to cluster and classify high-dimensional data. Experimental results of some applications show that the itFMA has good generalization capacity, offering a more robust and powerful performance than other competing approaches.