matchICA

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

matchICA function

<dl><dt>S</dt>
<dd>loading variable matrix</dd>
<dt>template</dt>
<dd>template for match</dd>
<dt>M</dt>
<dd>subject score matrix</dd></dl>

Arguments

match ICA — matchICA

<dl>

<dt>S</dt>
<dd>loading variable matrix</dd>


<dt>template</dt>
<dd>template for match</dd>


<dt>M</dt>
<dd>subject score matrix</dd>

</dl>

matchICA: match ICA

Description

Usage

Value

Arguments