gmmsslm: Semi-Supervised Gaussian Mixture Model with a Missing-Data
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.
||R (≥ 3.1.0), mvtnorm, stats, methods
||Ziyang Lyu [aut, cre],
Daniel Ahfock [aut],
Ryan Thompson [aut],
Geoffrey J. McLachlan [aut]
||Ziyang Lyu <ziyang.lyu at unsw.edu.au>
Please use the canonical form
to link to this page.