gen.inits

Generate initialization from specific space

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

gen.inits function

<dl><dt>p</dt>
<dd>p*p orthodox matrix</dd>
<dt>d</dt>
<dd>p*d orthodox matrix</dd>
<dt>runs</dt>
<dd>the number of orthodox matrix</dd>
<dt>orth.method</dt>
<dd>orthodox method</dd></dl>

Arguments

Generate initialization from specific space — gen.inits

<dl>

<dt>p</dt>
<dd>p*p orthodox matrix</dd>


<dt>d</dt>
<dd>p*d orthodox matrix</dd>


<dt>runs</dt>
<dd>the number of orthodox matrix</dd>


<dt>orth.method</dt>
<dd>orthodox method</dd>

</dl>

gen.inits: Generate initialization from specific space

Description

Usage

Value

Arguments

Examples