Title: Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data,
Abstract: In this paper, a scale mixture of Normal distributions model is developed for
classification and clustering of data having outliers and missing values. The
classification method, based on a mixture model, focuses on the introduction of
latent variables that gives us the possibility to handle sensitivity of model
to outliers and to allow a less restrictive modelling of missing data.
Inference is processed through a Variational Bayesian Approximation and a
Bayesian treatment is adopted for model learning, supervised classification and
clustering.