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.
| Version: |
1.1.5 |
| Depends: |
R (≥ 3.1.0), mvtnorm, stats, methods |
| Published: |
2023-10-16 |
| DOI: |
10.32614/CRAN.package.gmmsslm |
| Author: |
Ziyang Lyu [aut, cre],
Daniel Ahfock [aut],
Ryan Thompson [aut],
Geoffrey J. McLachlan [aut] |
| Maintainer: |
Ziyang Lyu <ziyang.lyu at unsw.edu.au> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
gmmsslm results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=gmmsslm
to link to this page.