cov2vec

Transform a variance matrix into a vector i.e., Sigma=R^T*R

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 

cov2vec function

<dl><dt>sigma</dt>
<dd>A \(p\times p\) variance matrix</dd></dl>

Arguments

Transform a variance matrix into a vector — cov2vec

<dl>

<dt>sigma</dt>
<dd>A \(p\times p\) variance matrix</dd>

</dl>

cov2vec: Transform a variance matrix into a vector

Description

Usage

Value

Arguments

Details