image_sparse

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

image_sparse function

<dl><dt>matrix</dt>
<dd>matrix of regression coefficients</dd>
<dt>main_</dt>
<dd>title</dd>
<dt>sub_</dt>
<dd>subtitle</dd>
<dt>col_names</dt>
<dd>columns names</dd></dl>

Arguments

Plot the image of a matrix — image_sparse

<dl>

<dt>matrix</dt>
<dd>matrix of regression coefficients</dd>


<dt>main_</dt>
<dd>title</dd>


<dt>sub_</dt>
<dd>subtitle</dd>


<dt>col_names</dt>
<dd>columns names</dd>

</dl>

image_sparse: Plot the image of a matrix

Description

Usage

Value

Arguments