GTM: the generative topographic mapping
Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
Robust mixture modelling using the t distribution
Statistics and Computing
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 mixture clustering using Pearson type VII distribution
Pattern Recognition Letters
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Recent results on robust density-based clustering have indicated that the uncertainty associated with the actual measurements can be exploited to locate objects that are atypical for a reason unrelated to measurement errors. In this paper, we develop a constrainedrobust mixture model, which, in addition, is able to nonlinearly map such data for visual exploration. Our robust visual mining approach aims to combine statistically sound density-based analysis with visual presentation of the density structure, and to provide visual support for the identification and exploration of `genuine' peculiar objects of interest that are not due to the measurement errors. In this model, an exact inference is not possible despite the latent space being discretised, and we resort to employing a structured variational EM. We present results on synthetic data as well as a real application, for visualising peculiar quasars from an astrophysical survey, given photometric measurements with errors.