I am not sure if this is the exact issue here, but note that data in
sklearn is represented row-wise: One data point per row. This means that
the feature names are indicated by the columns.
Now, after SVD, U is a matrix that lives in sample space and VT is a matrix
that lives in feature space. The only way to access "components" that live
in feature space is to use V. You will find the same thing in the PCA
objects.
Hope I am helping more than I am adding to confusion :)
Michael
On Fri, Dec 5, 2014 at 4:24 PM, João Felipe Santos <joao....@gmail.com>
wrote:
> Hi,
>
> I have been working recently with the factor analysis implementation in
> scikit-learn and checked it against its main reference (algorithm 21 in
> David Barber’s book). I noticed there is a difference from the algorithm
> described in the book in the way the SVD results are used. In the book, SVD
> is performed for the scaled data matrix like this: X = USV’, and then the
> first N columns in U and first N elements in S are used to compute the
> factors in each iteration (N being the number of components in the model).
> However, in scikit-learn code, the first N rows in V’ (or first N columns
> in V) are used instead. Is there any specific reason for using V instead of
> U in this case?
>
> Best regards,
> João
>
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