Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applied Computational Economics and Finance
Applied Computational Economics and Finance
Robust mixture modelling using the t distribution
Statistics and Computing
Bayesian inference in spherical linear models: robustness and conjugate analysis
Journal of Multivariate Analysis
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
Robust mixtures in the presence of measurement errors
Proceedings of the 24th international conference on Machine learning
Robust Visual Mining of Data with Error Information
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Robust Bayesian mixture modelling
Neurocomputing
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Hybrid fuzzy clustering using LP norms
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Single-frame image recovery using a Pearson type VII MRF
Neurocomputing
A robust EM clustering algorithm for Gaussian mixture models
Pattern Recognition
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A mixture of Student t-distributions (MoT) has been widely used to model multivariate data sets with atypical observations, or outliers for robust clustering. In this paper, we developed a novel robust clustering approach by modeling the data sets using mixture of Pearson type VII distributions (MoP). An EM algorithm is developed for the maximum likelihood estimation of the model parameters. An outlier detection criterion is derived from the EM solution. Controlled experimental results on the synthetic datasets show that the MoP is more viable than the MoT. The MoP performs comparably if not better, on average, in terms of outlier detection accuracy and out-of-sample log-likelihood with the MoT. Furthermore, we compared the performances of the Pearson type VII and the student t mixtures on the classification of several real pattern recognition data sets. The comparison favours the developed Pearson type VII mixtures.