Guillaume,
After glancing at the paper, I would be very, very interested in a Python
implementation for some work I'm doing. If you want to put it in a
separate repo, I'd be happy to help with beta testing/docstring.
I don't know much about the technique itself, so I cannot really help with
the actual algorithm implementation though.
Federico
On Thu, Nov 13, 2014 at 11:09 PM, Guillaume Dumas <
guillaume.du...@pasteur.fr> wrote:
> 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
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