@larsmans I followed your advice about the API. Unfortunately I wasn't able to extract additional insights from TruncatedSVD with regard to the failing test which is now exposed. Besides sign differences the two svd outputs give the same values inside the iteration. But the randomized svd's final score ends up being lower, as if it had only n_components - 1 or as if missing one iteration, hum ...

On 30.08.2013, at 10:22, Lars Buitinck <notifications@github.com> wrote:

> In sklearn/decomposition/factor_analysis.py:
> 
> > @@ -65,6 +67,9 @@ class FactorAnalysis(BaseEstimator, TransformerMixin):
> >          The initial guess of the noise variance for each feature.
> >          If None, it defaults to np.ones(n_features)
> >  
> > +    use_randomized_svd : int | bool
> > +        Whether to use randomized SVD or not. Defaults to False.
> For symmetry with TruncatedSVD, you might want to have a string-valued parameter algorithm instead. It may also be worthwhile to check that class's way of dealing with differences between SVD implems, and to try scipy.sparse.linalg.svds (which works on dense arrays too).
> 
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