normalize

Center the input matrix to mean 0 and scale to Euclidean length 1

Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal
Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
a novel algorithm for solving the sparse principal component analysis problem which provides
advantages over existing methods in terms of efficiency and convergence guarantees.
Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.

Zhong Zheng

amanpg

Alternating Manifold Proximal Gradient Method for Sparse PCA

Shixiang Chen

Justin Huang

Benjamin Jochem

Shiqian Ma

Haichuan Xu

Lingzhou Xue

Hui Zou

normalize function

<dl> <dt>x</dt>
<dd>matrix to be normalized</dd> <dt>center</dt>
<dd>centers the input matrix to mean 0 if TRUE, default if TRUE</dd> <dt>scale</dt>
<dd>scales the input matrix to Euclidean length 1 if TRUE, default is TRUE</dd></dl>

Arguments

Shixiang Chen, Justin Huang, Benjamin Jochem, Shiqian Ma, Lingzhou Xue and Hui Zou

Author

Matrix Normalization — normalize

<dl>

 <dt>x</dt>
<dd>matrix to be normalized</dd>

 <dt>center</dt>
<dd>centers the input matrix to mean 0 if TRUE, default if TRUE</dd>

 <dt>scale</dt>
<dd>scales the input matrix to Euclidean length 1 if TRUE, default is TRUE</dd>

</dl>

normalize: Matrix Normalization

Description

Usage

Value

Arguments

Author