gmmsslm: Semi-supervised Gaussian mixture modeling with a missing data mechanism in R

Semi-supervised learning is extensively applied these days to estimate classifiers from training data in which not all of the labels of the feature vectors are available. With the use of generative models that propose a form for the joint distribution of a feature vector and its ground-truth label, the Bayes' classifier can be estimated via maximum likelihood on partially classified training data. To increase the accuracy of this sample classifier, \cite{ahfock2020apparent} proposed that a missing-label mechanism be adopted and that the Bayes' classifier be estimated on the basis of the full likelihood formed in the framework that models the probability of a missing label given its observed feature vector in terms of its entropy. In the case of two Gaussian classes with a common covariance matrix, it was shown that the accuracy of the classifier so estimated from the partially classified training data can even have lower error rate than if it were estimated from the sample completely classified. Here, we focus on an algorithm for estimating the Bayes' classifier via the full likelihood in the case of multiple Gaussian classes with arbitrary covariance matrices. Different strategies for initializing the algorithm are discussed and illustrated. A new \proglang{R} package with these tools, \texttt{gmmsslm}, is demonstrated on real data.