whitener

Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.

Liangkang Wang

singR

Simultaneous Non-Gaussian Component Analysis

Irina Gaynanova

Benjamin Risk

whitener function

<dl><dt>X</dt>
<dd>dataset p x n.</dd>
<dt>n.comp</dt>
<dd>the number of components</dd>
<dt>center.row</dt>
<dd>whether center the row of data</dd></dl>

Arguments

Whitening Function — whitener

<dl>

<dt>X</dt>
<dd>dataset p x n.</dd>


<dt>n.comp</dt>
<dd>the number of components</dd>


<dt>center.row</dt>
<dd>whether center the row of data</dd>

</dl>

whitener: Whitening Function

Description

Usage

Value

Arguments