tiltedgaussian

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

tiltedgaussian function

<dl><dt>xData</dt>
<dd>input data</dd>
<dt>df</dt>
<dd>degree freedom</dd>
<dt>B</dt>
<dd>default value=100</dd>
<dt>...</dt>
<dd>ellipsis</dd></dl>

Arguments

tiltedgaussian — tiltedgaussian

<dl>

<dt>xData</dt>
<dd>input data</dd>


<dt>df</dt>
<dd>degree freedom</dd>


<dt>B</dt>
<dd>default value=100</dd>


<dt>...</dt>
<dd>ellipsis</dd>

</dl>

tiltedgaussian: tiltedgaussian

Description

Usage

Arguments