pca.svd: PCA compression by SVD decomposition

In gbonte/gbcode: Code from the handbook "Statistical foundations of machine learning"

PCA compression by SVD decomposition

Description

PCA compression by SVD decomposition

Usage

pca.svd(X, n.comp = 2)

Arguments

X:	input data
n.comp:	number of returned principal components

Details

PCA by SVD decomposition

PCA compression by SVD decomposition

Value

list

• data: transformed data

• vect: principal eigenvectors

• val: principal eigenvalues

• ord:

• mu: mean of original data

Author(s)

Gianluca Bontempi [e-mail]

References

Handbook Statistical foundations of machine learning available in http://www.ulb.ac.be/di/map/gbonte/mod_stoch/syl.pdf

Examples

N<-100
n<-5
neff<-3
R<-regrDataset(N,n,neff,0.1)
X<-R$X
Z<-pca.svd(X,3)$data

gbonte/gbcode documentation built on Feb. 27, 2024, 7:38 a.m.