The infinite hidden Markov random field model
IEEE Transactions on Neural Networks
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Variational Bayesian mixture of robust CCA models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Expert Systems with Applications: An International Journal
A nonparametric Bayesian approach toward robot learning by demonstration
Robotics and Autonomous Systems
A spatially-constrained normalized Gamma process prior
Expert Systems with Applications: An International Journal
Nonparametric bayesian multitask collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Factor analysis is a statistical covariance modeling technique based on the assumption of normally distributed data. A mixture of factor analyzers can be hence viewed as a special case of Gaussian (normal) mixture models providing a mathematically sound framework for attribute space dimensionality reduction. A significant shortcoming of mixtures of factor analyzers is the vulnerability of normal distributions to outliers. Recently, the replacement of normal distributions with the heavier-tailed Student's-t distributions has been proposed as a way to mitigate these shortcomings and the treatment of the resulting model under an expectation-maximization (EM) algorithm framework has been conducted. In this paper, we develop a Bayesian approach to factor analysis modeling based on Student's-t distributions. We derive a tractable variational inference algorithm for this model by expressing the Student's-t distributed factor analyzers as a marginalization over additional latent variables. Our innovative approach provides an efficient and more robust alternative to EM-based methods, resolving their singularity and overfitting proneness problems, while allowing for the automatic determination of the optimal model size. We demonstrate the superiority of the proposed model over well-known covariance modeling techniques in a wide range of signal processing applications.