kb0000 (9) [Avatar] Offline
At the end of section 4.5.5, the Tip box recommends discarding the eigenvalues, but there is no Sigma matrix in the code examples in the section and it is not obvious to me how to discard them properly to avoid the situation the tip describes. The scikit-learn docs for TruncatedSVD.fit_transform() also do not offer much clue.

Might we get a code snippet that shows how to do this for each of the named implementations (LSA, PCA, and SVD)? If this is done elsewhere, a note mentioning that would work as well.
428125 (18) [Avatar] Offline
Even thought TruncatedSVD does not ignore eigenvalues, if the resulting topic vectors are normalized to unit length that normalization accomplishes the same thing as ignoring the scaling by the sigma matrix in the first place. I will add a details to make this more clear.