gmmsslm - Semi-Supervised Gaussian Mixture Model with a Missing-Data
Mechanism
The algorithm of semi-supervised learning is based on
finite Gaussian mixture models and includes a mechanism for
handling missing data. It aims to fit a g-class Gaussian
mixture model using maximum likelihood. The algorithm treats
the labels of unclassified features as missing data, building
on the framework introduced by Rubin (1976)
<doi:10.2307/2335739> for missing data analysis. By taking into
account the dependencies in the missing pattern, the algorithm
provides more information for determining the optimal
classifier, as specified by Bayes' rule.
