normalise_logprob

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.

Ziyang Lyu

gmmsslm

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

Ziyang Lyu 

Daniel Ahfock 

Ryan Thompson 

Geoffrey J. McLachlan 

normalise_logprob function

<dl><dt>x</dt>
<dd>A variable vector.</dd></dl>

Arguments

Normalize log-probability — normalise_logprob

<dl>

<dt>x</dt>
<dd>A variable vector.</dd>

</dl>

Normalize log-probability

normalise_logprob: Normalize log-probability

Description

Usage

Value

Arguments