Hello Siddharth

Sorry for the late reply.

The list in the link you provide seems to be very eclectic, some
methods being very different than others in nature, only having in
common the fact that they can do dimensionality reduction.

I think that a solid GSoC proposal should be very specific. Among the
algorithms in the list that we don't have (and maybe not only from the
list), you should choose the ones that you think would be most useful
and that you can understand best.  It would help greatly if you have
"hidden interests" to use your implementation in the future for your
own work.

Could you please clarify about what specific algorithms you would be
interested in, and which ones you are most familiar with?

Yours,
Vlad

On Fri, Apr 12, 2013 at 7:14 PM, Siddharth <[email protected]> wrote:
> Hi,
>
> I am currently pursuing MS by Research in the field of image processing and
> machine learning at IIIT- Hyderabad, India. I am interested in working for
> Scikit-learn this summer in a GSoC project.
>
> I propose to implement techniques for dimensionality reduction and metric
> learning which are currently missing in scikit-learn.
>
> http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
>
> Some of the techniques mentioned in the above link have not been
> implemented. I think this will be a good addition to scikit-learn.
>
> There are also many kernel functions which have not been implemented.
>
> http://crsouza.blogspot.in/2010/03/kernel-functions-for-machine-learning.html
>
> What are your views about this?
>
> Best Regards,
> Siddharth
>
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