pca: PCA compression
In gbonte/gbcode: Code from the handbook "Statistical foundations of machine learning"
pca R Documentation
PCA compression
Description
PCA compression
Usage
pca(X, n.comp = 2)
Arguments
X:
input data
n.comp:
number of returned principal components
Details
PCA
PCA compression
Value
list
data: transformed data
vect: principal eigenvectors
val: principal eigenvalues
ord:
mu: mean of original data
Author(s)
Gianluca Bontempi gbonte@ulb.ac.be
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$X
Z<-pca(X,3)$data
gbonte/gbcode documentation built on Feb. 27, 2024, 7:38 a.m.
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| pca | R Documentation |
PCA compression
Description
PCA compression
Usage
pca(X, n.comp = 2)
Arguments
X: |
input data |
n.comp: |
number of returned principal components |
Details
PCA
PCA compression
Value
list
data: transformed data
vect: principal eigenvectors
val: principal eigenvalues
ord:
mu: mean of original data
Author(s)
Gianluca Bontempi gbonte@ulb.ac.be
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$X
Z<-pca(X,3)$data
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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