Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism
Bayes' rule of allocation
Bootstrap Analysis for gmmsslm
Transform a variance matrix into a vector
Discriminant function
Error rate of the Bayes rule for a g-class Gaussian mixture model
Error rate of the Bayes rule for two-class Gaussian homoscedastic mode...
Posterior probability
Shannon entropy
Fitting Gaussian mixture model to a complete classified dataset or an ...
gmmsslmFit Class
Initial values for ECM
Transfer a list into a vector
Full log-likelihood function
Log likelihood for partially classified data with ingoring the missing...
Log likelihood function formed on the basis of the missing-label indic...
log summation of exponential function
Label matrix
Negative objective function for gmmssl
Normalize log-probability
Transfer a vector into a list
Extract parameter list from gmmsslmFit objects
Plot Missingness Mechanism and Boxplot
Predict unclassified label
Transfer a probability vector into a vector
Generation of a missing-data indicator
Normal mixture model generator.
Summary method for gmmsslmFit objects
Transform a vector into a matrix
Transfer an informative vector to a probability vector
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.