I guess knowing the max alpha is useful to know where to start your grid
search from. However, I think deriving max alpha for NMF should be more
difficult since the problem is non-convex.
Mathieu
On Wed, Feb 3, 2016 at 7:40 AM, Vlad Niculae <zephy...@gmail.com> wrote:
> Hi James,
>
> I'm not sure how useful a minimum alpha would be. Even if no weights
> are shrunk quite to zero, the regularization can still impact
> performance metrics. I would be curious what application you have in
> mind for this.
>
> The max alpha question is interesting, I am curious as well. (Sorry my
> reply can't be more helpful!)
>
> Yours,
> Vlad
>
> On Tue, Feb 2, 2016 at 3:45 PM, James Jensen <jdjen...@eng.ucsd.edu>
> wrote:
> > For ElasticNetCV, inside the function _alpha_grid() it computes the
> maximum
> > regularization strength alpha, with a given dataset X, target Y, and L1
> > ratio, for which there will be at least one nonzero coefficient. I'm
> > wondering if/how the same could be computed for sklearn's
> L1/L2-regularized
> > NMF. I'm also interested in computing a minimum alpha (the smallest at
> which
> > there are more nonzero coefficients than with alpha=0).
> >
> > Does anyone know how this could be done?
> >
> > Thanks,
> >
> > James Jensen
> > PhD student, Bioinformatics and Systems Biology
> > Trey Ideker lab
> > University of California, San Diego
> >
> >
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