EMMIXSSL: Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism

The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayes’ rule.

Version: 1.1.1
Depends: R (≥ 3.1.0), mvtnorm, stats
Published: 2022-10-18
Author: Ziyang Lyu, Daniel Ahfock, Geoffrey J. McLachlan
Maintainer: Ziyang Lyu <ziyang.lyu at unsw.edu.au>
License: GPL-3
NeedsCompilation: no
CRAN checks: EMMIXSSL results


Reference manual: EMMIXSSL.pdf


Package source: EMMIXSSL_1.1.1.tar.gz
Windows binaries: r-devel: EMMIXSSL_1.1.1.zip, r-release: EMMIXSSL_1.1.1.zip, r-oldrel: EMMIXSSL_1.1.1.zip
macOS binaries: r-release (arm64): EMMIXSSL_1.1.1.tgz, r-oldrel (arm64): EMMIXSSL_1.1.1.tgz, r-release (x86_64): EMMIXSSL_1.1.1.tgz, r-oldrel (x86_64): EMMIXSSL_1.1.1.tgz
Old sources: EMMIXSSL archive


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