Get eigenvalues with Incremental PCA

Question

I'm working with dimensionality reduction and would like to get eigenvalues and eigenvectors from my dataset. Since there are several features (Images) I tried to use Incrementa PCA, but I did not find a way to get the eigenvalues / eigenvectors in the documentation, is it possible to get them with the incremental PCA?

def get_incremental_pca(training,n_components,batch_size):

   ipca = PCA(n_components)
   return ipca.fit_transform(training) //The training set with reduced dimensionality

Please add a code that you tried here. – Miheretab Alemu Feb 20 '17 at 20:33 — Miheretab Alemu, Feb 20 '17 at 20:33
Did you get the answer? I faced the same problem. – user3503711 Jun 22 '20 at 20:44 — user3503711, Jun 22 '20 at 20:44

score 0 · Answer 1 · answered Oct 03 '21 at 23:16

0

There's not much code to go off of here, so in general cases:

eigenvalues = ipca.explained_variance_ratio_

eigenvectors = ipca.components_

mu = ipca.mean_

answered Oct 03 '21 at 23:16

George McKinley

23
3

Get eigenvalues with Incremental PCA

1 Answers1