Dear Liu Benuyan.
Thank you for offering to contribute to scikit-learn.
I am no expert in sparse signal recovery and/or matrix factorization,
so I can not really comment on the method.
I just wanted to mention that we include mostly widely-used or classical
algorithms.
I am not sure how far this is true for the method you are interested in.
It looks to me like it was published only recently (in 2012). Is that
correct?
Maybe Alex and Gael (or someone else?) know more about the algorithm and
have a better idea of how it might fit into scikit-learn.
Best,
Andy
On 11/28/2012 07:08 AM, [email protected] wrote:
Dear scikit-learn community:
Block Sparse Bayesian Learning is a powerful CS algorithm for recovering block
sparse signals with structures, and shows the additional benefits of
reconstruct non-sparse signals, see Dr. zhilin zhang's websites:
http://dsp.ucsd.edu/~zhilin/BSBL.html
<http://dsp.ucsd.edu/%7Ezhilin/BSBL.html>
I currently implement the BSBL-BO algorithm by Zhang and a fast version of BSBL
algorithm recently proposed by us, called BSBL-FM, in python. Plus many demos
using these two codes. Does scikit-learn community welcome such type of code ?
what is the procedure to submit the code in the mainstream of scikit learn?
Thanks for the great project!
Liu benyuan
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