DPPC

Distributed projection principal component

Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods and Farm Test methods to these models. Evaluates the efficacy of these methods within the context of Laplace factor models by scrutinizing parameter estimation accuracy, mean square error, and the degree of sparsity.

Guangbao Guo

Laplace Factor Model Analysis and Evaluation

Siqi Liu

DPPC function

<dl><dt>data</dt>
<dd>is a total data set</dd>
<dt>m</dt>
<dd>is the number of principal component</dd>
<dt>n1</dt>
<dd>is the length of each data subset</dd>
<dt>K</dt>
<dd>is the number of nodes</dd></dl>

Arguments

Distributed projection principal component — DPPC

<dl>

<dt>data</dt>
<dd>is a total data set</dd>


<dt>m</dt>
<dd>is the number of principal component</dd>


<dt>n1</dt>
<dd>is the length of each data subset</dd>


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

</dl>

DPPC: Distributed projection principal component

Description

Usage

Value

Arguments

Examples