I had a need for a non-negative logistic classifier a while back, and wrote
a light-weight function that does the optimization directly, along with an
L2 regularizer. The code is on gist: https://gist.github.com/1730797
-Marc
On Fri, Feb 3, 2012 at 9:55 AM, Vlad Niculae <[email protected]> wrote:
> A nice idea would be to extend the scipy NNLS in the ways needed to use it
> in scikit-learn's NMF instead of the _nls_subproblem code translated from
> C.J. Lin's code.
>
> The scipy NNLS is written in Fortran. I'd like to bench _nls_subproblem
> against it.
> Maybe we could have a cython projected sgd non-negative least square
> method with L1 constraints that would support sparse data as well? We
> discussed this idea before, but would it be an NNLS solver?
>
> Would this lead to a sparse NMF?
>
> Vlad
>
> On Feb 3, 2012, at 11:32 , Mathieu Blondel wrote:
>
> > For non-negative least-squares, you can use this:
> >
> >
> http://docs.scipy.org/doc/scipy-0.7.x/reference/generated/scipy.optimize.nnls.html
> >
> > We could also add an estimator that implements fit and predict in
> > scikit-learn (although the above function doesn't support sparse
> > matrices :$)
> >
> > Mathieu
> >
> >
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