pca.svd | R Documentation |
PCA compression by SVD decomposition
pca.svd(X, n.comp = 2)
X: |
input data |
n.comp: |
number of returned principal components |
PCA by SVD decomposition
PCA compression by SVD decomposition
list
data: transformed data
vect: principal eigenvectors
val: principal eigenvalues
ord:
mu: mean of original data
Gianluca Bontempi gbonte@ulb.ac.be
Handbook Statistical foundations of machine learning available in http://www.ulb.ac.be/di/map/gbonte/mod_stoch/syl.pdf
N<-100
n<-5
neff<-3
R<-regrDataset(N,n,neff,0.1)
X<-R$X
Z<-pca.svd(X,3)$data
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