Neural Computation
An introduction to variational methods for graphical models
Learning in graphical models
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
The Journal of Machine Learning Research
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Neural Networks
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Robust mixtures in the presence of measurement errors
Proceedings of the 24th international conference on Machine learning
Robust Nonparametric Probability Density Estimation by Soft Clustering
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
The mixtures of Student's t-distributions as a robust framework for rigid registration
Image and Vision Computing
Letters: A novel view of the variational Bayesian clustering
Neurocomputing
IEEE Transactions on Signal Processing
Multiclass probabilistic kernel discriminant analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multivariate Student-t self-organizing maps
Neural Networks
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Probability density estimation with tunable kernels using orthogonal forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Robust mixture clustering using Pearson type VII distribution
Pattern Recognition Letters
Variational inference for Student-t MLP models
Neurocomputing
Robust curve clustering based on a multivariate t-distribution model
IEEE Transactions on Neural Networks
The infinite Student's t-mixture for robust modeling
Signal Processing
A new variational Bayesian algorithm with application to human mobility pattern modeling
Statistics and Computing
Robust Bayesian Clustering for Replicated Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A variational Bayesian approach to robust sensor fusion based on Student-t distribution
Information Sciences: an International Journal
Review: A review of novelty detection
Signal Processing
A comparative study of novel robust clustering algorithms
Intelligent Data Analysis
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Bayesian approaches to density estimation and clustering using mixture distributions allow the automatic determination of the number of components in the mixture. Previous treatments have focussed on mixtures having Gaussian components, but these are well known to be sensitive to outliers, which can lead to excessive sensitivity to small numbers of data points and consequent over-estimates of the number of components. In this paper we develop a Bayesian approach to mixture modelling based on Student-t distributions, which are heavier tailed than Gaussians and hence more robust. By expressing the Student-t distribution as a marginalization over additional latent variables we are able to derive a tractable variational inference algorithm for this model, which includes Gaussian mixtures as a special case. Results on a variety of real data sets demonstrate the improved robustness of our approach.