whitener

The function for perform whitening.

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

whitener function

<dl><dt>X</dt>
<dd>The data matrix with dimension P x T.</dd>
<dt>n.comp</dt>
<dd>The number of components.</dd>
<dt>center.row</dt>
<dd>Whether to center the row of data. Default is FALSE.</dd>
<dt>use_irlba</dt>
<dd>Whether to use the irlba method to perform fast truncated singular value decomposition in whitening step, helpful for memorying intermediate dataset. Default is TRUE.</dd></dl>

Arguments

The function for perform whitening. — whitener

<dl>

<dt>X</dt>
<dd>The data matrix with dimension P x T.</dd>


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


<dt>center.row</dt>
<dd>Whether to center the row of data. Default is FALSE.</dd>


<dt>use_irlba</dt>
<dd>Whether to use the irlba method to perform fast truncated singular value decomposition in whitening step, helpful for memorying intermediate dataset. Default is TRUE.</dd>

</dl>

whitener: The function for perform whitening.

Description

Usage

Value

Arguments