gen.inits

Function for generating random starting points

Provides an implementation of the Sparse ICA method in Wang et al. (2024) <doi:10.1080/01621459.2024.2370593> for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency.

Zihang Wang

SparseICA

Sparse Independent Component Analysis

Irina Gaynanova

Aleksandr Aravkin

Benjamin Risk

gen.inits function

<dl><dt>p</dt>
<dd>The number of rows.</dd>
<dt>d</dt>
<dd>The number of columns.</dd>
<dt>runs</dt>
<dd>The number of random starts.</dd>
<dt>orth.method</dt>
<dd>The method used for generating initial values of U matrix. The default is "svd".</dd></dl>

Arguments

Function for generating random starting points — gen.inits

<dl>

<dt>p</dt>
<dd>The number of rows.</dd>


<dt>d</dt>
<dd>The number of columns.</dd>


<dt>runs</dt>
<dd>The number of random starts.</dd>


<dt>orth.method</dt>
<dd>The method used for generating initial values of U matrix. The default is "svd".</dd>

</dl>

gen.inits: Function for generating random starting points

Description

Usage

Value

Arguments