R help - Sep 2011 - Newer Matrix Factorization Techniques

Hi,

I am not sure if this should go to r-help or r-dev list. I have looked
at some archives of R libraries but cannot seem to see a project that
focuses on the new matrix factorization techniques that are showing up
in the literature. I have made a list of them:

https://sites.google.com/site/igorcarron2/matrixfactorizations

they include Robust PCA, Dictionnary Learning, Sparse PCA and are
mostly implemented in Matlab. What is most interesting is that even
though they share the same names with techniques already listed in
some R libraries, they really are implementing very different
algorithms that perform better.

 Here are is question: Is there a repository in the R-world somewhere
that lists new implementations of an algorithm like say robust PCA ?

Thanks in advance,

Igor.

------------------------
Igor Carron, Ph.D.
http://nuit-blanche.blogspot.com

Bioconductor has the pcaMethods package, with multiple options for PCA,
including a robust L1-norm SVD.

Kevin Wright


On Thu, Sep 1, 2011 at 5:16 AM, Igor Carron <igorcarron@gmail.com> wrote:
> Hi,
>
> I am not sure if this should go to r-help or r-dev list. I have looked
> at some archives of R libraries but cannot seem to see a project that
> focuses on the new matrix factorization techniques that are showing up
> in the literature. I have made a list of them:
>
> https://sites.google.com/site/igorcarron2/matrixfactorizations
>
> they include Robust PCA, Dictionnary Learning, Sparse PCA and are
> mostly implemented in Matlab. What is most interesting is that even
> though they share the same names with techniques already listed in
> some R libraries, they really are implementing very different
> algorithms that perform better.
>
>  Here are is question: Is there a repository in the R-world somewhere
> that lists new implementations of an algorithm like say robust PCA ?
>
> Thanks in advance,
>
> Igor.
>
> ------------------------
> Igor Carron, Ph.D.
> http://nuit-blanche.blogspot.com
>
> ______________________________________________
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