Compute D, L, and C in the Fisher optimal partitions algorithm

Ordered homogeneity pursuit lasso (OHPL)
algorithm for group variable selection proposed in Lin et al. (2017)
<DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
homogeneity structure in high-dimensional data and enjoys the
grouping effect to select groups of important variables
automatically. This feature makes it particularly useful for
high-dimensional datasets with strongly correlated variables,
such as spectroscopic data.

Nan Xiao

OHPL

Ordered Homogeneity Pursuit Lasso for Group Variable Selection

You-Wu Lin

dlc function

<dl><dt>X</dt>
<dd>A set of samples.</dd>
<dt>maxk</dt>
<dd>Maximum number of <code>k</code>.</dd></dl>

Arguments

Compute D, L, and C in the Fisher optimal partitions algorithm — dlc

<dl>

<dt>X</dt>
<dd>A set of samples.</dd>


<dt>maxk</dt>
<dd>Maximum number of <code>k</code>.</dd>

</dl>

dlc: Compute D, L, and C in the Fisher optimal partitions algorithm

Description

Usage

Value

Arguments