Normalizing before PCA Lowrank?

Hi! I would like to ask for advice on the PCA Lowrank (torch.pca_lowrank — PyTorch 1.9.0 documentation)…

  • Do I have to normalize my features along the feature dimension before?

  • I am a little confused because there is an input option where it said center=True … Does this option include whitening the input features?

It would be nice if someone can point out how to normalize A… A is [*,m,n] .

m = samples and n = features

U, S, V = torch.pca_lowrank(A)
k   = 3
A_k = torch.matmul(A, V[:, :k])