Returns the leading sparse principal component of a matrix using the truncated power method.

Implements an algorithm for computing multiple sparse principal
components of a dataset. The method is based on Cory-Wright and Pauphilet
"Sparse PCA with Multiple Principal Components" (2022)
<doi:10.48550/arXiv.2209.14790>. The algorithm uses an iterative deflation
heuristic with a truncated power method applied at each iteration to compute
sparse principal components with controlled sparsity.

Jean Pauphilet

msPCA

Sparse Principal Component Analysis with Multiple Principal
Components

Ryan Cory-Wright

tpw function

<dl><dt>Sigma</dt>
<dd>A matrix. The correlation or covariance matrix, whose sparse PCs will be computed.</dd>
<dt>k</dt>
<dd>An integer. Target sparsity of the PC.</dd>
<dt>maxIter</dt>
<dd>(optional) An integer. Maximum number of iterations of the algorithm. Default 200.</dd>
<dt>verbose</dt>
<dd>(optional) A Boolean. Controls console output. Default TRUE.</dd>
<dt>timeLimit</dt>
<dd>(optional) An integer. Maximum time in seconds. Default 10.</dd></dl>

Arguments

Truncated Power Method — tpw

<dl>

<dt>Sigma</dt>
<dd>A matrix. The correlation or covariance matrix, whose sparse PCs will be computed.</dd>


<dt>k</dt>
<dd>An integer. Target sparsity of the PC.</dd>


<dt>maxIter</dt>
<dd>(optional) An integer. Maximum number of iterations of the algorithm. Default 200.</dd>


<dt>verbose</dt>
<dd>(optional) A Boolean. Controls console output. Default TRUE.</dd>


<dt>timeLimit</dt>
<dd>(optional) An integer. Maximum time in seconds. Default 10.</dd>

</dl>

tpw: Truncated Power Method

Description

Usage

Value

Arguments

References

Examples