gmmsslm (Q73783)
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Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | gmmsslm |
Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism |
Statements
16 October 2023
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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.
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