signchange

Change the sign of S and M matrices to positive skewness.

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

signchange function

<dl><dt>S</dt>
<dd>The S matrix with dimension P x Q.</dd>
<dt>M</dt>
<dd>The M matrix with dimension Q x T.</dd></dl>

Arguments

Change the sign of S and M matrices to positive skewness. — signchange

<dl>

<dt>S</dt>
<dd>The S matrix with dimension P x Q.</dd>


<dt>M</dt>
<dd>The M matrix with dimension Q x T.</dd>

</dl>

signchange: Change the sign of S and M matrices to positive skewness.

Description

Usage

Value

Arguments