An Introduction to Variational Methods for Graphical Models
Machine Learning
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust mixture modelling using the t distribution
Statistics and Computing
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Accelerated EM-based clustering of large data sets
Data Mining and Knowledge Discovery
Neural Networks
Robust mixtures in the presence of measurement errors
Proceedings of the 24th international conference on Machine learning
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Letters: A novel view of the variational Bayesian clustering
Neurocomputing
Robust Bayesian mixture modelling
Neurocomputing
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Both parametric Bayesian mixture and non-parametric Dirichlet process mixture modelling DPM approaches for density estimation and clustering allow for automatic model selection. It is interesting to study which approach can better fit the data. In this paper, we focus on robust clustering taking the Student t-distribution as the building block. We develop two novel robust clustering algorithms, one using Type-IV Student t-distribution mixture modelling SMM and one robust DPM RDPM, and explain them in detail. The new algorithms are compared using controlled experiment settings and benchmark UCI datasets, in terms of commonly-used internal and external cluster validity indices. Experimental results show that Type-IV SMM shows comparable performance to Type-II SMM, while additionally identifying outliers, and that RDPM outperforms conventional DPM. When comparing the two new algorithms with each other, they are found to perform comparably, but Type-IV SMM is less sensitive to initialisation and has a better generalisation ability. Hence, it is recommended to use Type-IV SMM for robust clustering and model selection.