Hi Liu, This work is really nice and very fancy, but it is also very recent and needs a bit more insight and benchmarking before it can enter scikit-learn: we have a rule not to integrate any new approach that is more than 2 years old. The reason is that if the approach is to be a massive success, it will be well-studied and thus it pays to wait a bit to understand the trade-offs better. A good approach is to put the implementation that you have on github in a separate repo and advertise it so that people use it and improve it. As it becomes more and more used, you'll get feedback on it, and we'll gather insight, so that we can planify a merge or not, depending on the community pick up.
There are well-known and fundamental algorithms to deal with similar problems that are not in the scikit-learn yet, such as group lasso. From what I understand of the block sparse bayesian learning, could benefit from a group lasso solver. Thus integrating a group lasso would be a useful step to deal with block-sparse problems. Fabian (@fabianp on github) has started work on a group lasso solver that is on his github account, but we never could find time to do the integration. I am not sure if it does overlapping groups, and thus if you can use it, but it would be nice to work on its integration in the scikit-learn. Documentation and examples need to be written. Thanks a lot for your interest. Gaël On Wed, Nov 28, 2012 at 03:08:23PM +0800, [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 > 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 > ------------------------------------------------------------------------------ > Keep yourself connected to Go Parallel: > INSIGHTS What's next for parallel hardware, programming and related areas? > Interviews and blogs by thought leaders keep you ahead of the curve. > http://goparallel.sourceforge.net > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Gael Varoquaux Researcher, INRIA Parietal Laboratoire de Neuro-Imagerie Assistee par Ordinateur NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux ------------------------------------------------------------------------------ Keep yourself connected to Go Parallel: INSIGHTS What's next for parallel hardware, programming and related areas? Interviews and blogs by thought leaders keep you ahead of the curve. http://goparallel.sourceforge.net _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
