João Felipe Santos
wrote:
> To give this a bit of context, I implemented Barber’s algorithm in Julia
> and used the scikit-learn implementation to check my results (and also to
> get hints for fixing numerical instability issues). I think the main
> source of confusion here was that both in stand
Hi Michael and Kyle,
thanks to you both for the fast replies!
(and Kyle, nice to see you here too!)
To give this a bit of context, I implemented Barber’s algorithm in Julia and
used the scikit-learn implementation to check my results (and also to get hints
for fixing numerical instability iss
I haven't looked closely, but is Barber's data format considered to be
examples as columns, or examples as rows? That difference is usually what I
see in a bunch of different SVD based algorithms. It is very annoying when
reading the literature.
aka.
what Michael said
On Fri, Dec 5, 2014 at 10:2
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
