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succeeds
2012 Apr 24
1
In robust PCA methods, how to get variance explained?
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!