Hi! I would like to ask for advice on the PCA Lowrank (torch.pca_lowrank — PyTorch 1.9.0 documentation)…
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Do I have to normalize my features along the feature dimension before?
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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])