[mlpack] regression using sparse matrices/vectors

[Darryl Reeves]

Hi Ryan,

Thanks for the quick response. This is helpful. I really like the project
and was worried that I might have to go in a different direction to find a
solution for my problem. It is totally fine if the parameters of my model
are not sparse. However, due to the size of the data that I'm working with,
its not feasible for me to do the regression using the dense representation
of the predictor matrix. Can you provide some direction on which code
changes would be required to templatize the regression algorithms?

Thanks,
Darryl


On Tue, Dec 10, 2013 at 12:09 PM, Ryan Curtin <gth671b at mail.gatech.edu>wrote:

> On Mon, Dec 09, 2013 at 08:21:48PM -0500, Darryl Reeves wrote:
> > Hello,
> >
> > I am interested in using mlpack in a project that I am working on. I know
> > that there is some support for sparse matrices in the project but can you
> > let me know if this support is available for mlpack's regression methods?
>
> Hello Darryl,
>
> There are a couple ways sparse matrices could be used in the context of
> regression.  If your input data features are sparse, then this is pretty
> simple (although some minor code modification needs to be done to
> templatize the regression algorithms so you can use a sparse matrix).
> But the parameters of the model will not be sparse.
>
> On the other hand, if you are looking for a sparse parameter vector, you
> could use LARS with L1 regularization.
>
> Does this help?
>
> Thanks,
>
> Ryan
>
> --
> Ryan Curtin       | "Weeee!"
> ryan at igglybob.com |   - Bobby
>



-- 
Darryl Reeves
Ph.D. Candidate
Mason Lab
Weill Cornell Medical College of Cornell University
Tri-Institutional Program in Computational Biology and Medicine
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