For example,
PcaHubert,

how to get the variance explained which are similar to those concepts in
traditional PCA?

In traditional PCA, you have a bunch of eigenvalue lambdas...

and you sort the lambdas from the biggest to the smallest,

the lambda_i / (sum of all lambdas) is the variance explained by that
principal component...

how to obtain the equivalent concepts in PcaHubert?

Thanks a lot!

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