Dear Dav,
NNMF is indeed widely used and it is already implemented in scikit-learn:
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html
I was proposing to add the Graph Regularized extension that start to get
attention in genomics, also known as Network Based Stratification (see this
Nature Methods for details:
http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html)
But for now, indeed, there is maybe not enough papers using this method.
Hopefully I will have a pull request when there will ;)
Anybody wanting to help is of course much welcome.
Thanks to the scikit-learn team for their great contribution by the way!
Best,
Guillaume
On Thu, Nov 13, 2014 at 10:05 PM, Dav Clark <davcl...@berkeley.edu> wrote:
> I mostly just lurk here, but I figure I may be one of few folks who knows
> much about the motor control world, where NNMF is highly useful. You can
> find a nice motivation of why NNMF is useful here:
>
>
> https://neurolab.gatech.edu/wp/wp-content/uploads/ting/papers/Ting%20and%20Chvatal%202010.pdf
>
> Basically, it's useful for folks who are interested in the boundaries of a
> system (as is the case with the dynamics of motor control - only certain
> configurations of a physiological body are possible). Movement science and
> neuroscience folks are mostly using matlab still.
>
> There also appear to be many highly cited papers in machine vision. So, I
> suspect this would be useful!
>
> Best,
> D
>
> On Thu, Nov 13, 2014 at 5:13 AM, Guillaume Dumas <
> guillaume.du...@pasteur.fr> wrote:
>
>> Ok, let's wait a little then.
>> Thanks for your feebacks Gaël and Alex!
>>
>> Guillaume
>>
>> On Thu, Nov 13, 2014 at 11:06 AM, Gael Varoquaux <
>> gael.varoqu...@normalesup.org> wrote:
>>
>>> On Thu, Nov 13, 2014 at 10:47:52AM +0100, Guillaume Dumas wrote:
>>> > I thus wanted to first check with core dev team if this feature would
>>> > be useful for scikit-learn, if anybody is already working on it, and if
>>> > not if there are people who may be interested to help me checking and
>>> > adapting the code for then make a correct pull-request.
>>>
>>> First you have to convince us that there is a strong interest in the
>>> method. In particular, that there is a strongly-cited paper, that there
>>> are a lot of usecases, and that the method will fit in the API:
>>>
>>> http://scikit-learn.org/stable/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published
>>>
>>> Choosing whether to include a method or not is a delicate choice.
>>>
>>> Gaël
>>>
>>>
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>
>
> --
> Dav Clark
> Data Scientist
> Berkeley D-Lab + BIDS
> bead.glass
> 510-664-7000
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