dist_beta

Compute distance matrix between regression vectors

Inference of Multiscale graphical models with neighborhood
selection approach. The method is based on solving a convex
optimization problem combining a Lasso and fused-group Lasso
penalties. This allows to infer simultaneously a conditional
independence graph and a clustering partition. The optimization is
based on the Continuation with Nesterov smoothing in a
Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
<doi:10.1109/TMI.2018.2829802> implemented in python.

Edmond Sanou

mglasso

Multiscale Graphical Lasso

Tung Le

Christophe Ambroise

Geneviève Robin

dist_beta function

<dl><dt>beta</dt>
<dd>matrix of regression vectors</dd>
<dt>distance</dt>
<dd>euclidean or relative distance</dd></dl>

Arguments

Compute distance matrix between regression vectors — dist_beta

<dl>

<dt>beta</dt>
<dd>matrix of regression vectors</dd>


<dt>distance</dt>
<dd>euclidean or relative distance</dd>

</dl>

dist_beta: Compute distance matrix between regression vectors

Description

Usage

Value

Arguments