Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data
Machine Learning
2017-11-23 v1
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.
Cite
@article{arxiv.1711.08374,
title = {Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data},
author = {G. Revillon and A. Djafari and C. Enderli},
journal= {arXiv preprint arXiv:1711.08374},
year = {2017}
}