Drpca

We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2025) <doi:10.1016/j.physa.2024.130308>.

Guangbao Guo

FPCdpca

The FPCdpca Criterion on Distributed Principal Component
Analysis

Jiarui Li

Drpca function

<dl><dt>data</dt>
<dd>is sparse random projection matrix</dd>
<dt>K</dt>
<dd>is the number of distributed nodes.</dd>
<dt>nk</dt>
<dd>is the size of subsets.</dd>
<dt>d</dt>
<dd>is the dimension number.
n is the sample size.
p the number of variables.</dd></dl>

Arguments

Distributed random PCA — Drpca

<dl>

<dt>data</dt>
<dd>is sparse random projection matrix</dd>


<dt>K</dt>
<dd>is the number of distributed nodes.</dd>


<dt>nk</dt>
<dd>is the size of subsets.</dd>


<dt>d</dt>
<dd>is the dimension number.
n is the sample size.
p the number of variables.</dd>

</dl>

Drpca: Distributed random PCA

Description

Usage

Value

Arguments

Examples