vec2cov

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

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.

Ziyang Lyu

EMMIXSSL

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

vec2cov function

<dl><dt>par</dt>
<dd>A vector representing a variance matrix</dd></dl>

Arguments

Transform a vector into a matrix — vec2cov

<dl>

<dt>par</dt>
<dd>A vector representing a variance matrix</dd>

</dl>

vec2cov: Transform a vector into a matrix

Description

Usage

Value

Arguments

Details