2012/11/29 :
> Hi, Granfort,
>
> Currently I am implementing the BSBL-EM and BSBL-l1 algorithm by Zhang . The
> effort is to maintain compatibility with the original calling conversion of
> Zhang's TSP2012 paper. I am totally new to python and struggling in the
> matrix/array conversion, matrix
Hi, Granfort,
Currently I am implementing the BSBL-EM and BSBL-l1 algorithm by Zhang . The
effort is to maintain compatibility with the original calling conversion of
Zhang's TSP2012 paper. I am totally new to python and struggling in the
matrix/array conversion, matrix/array concatenate and in
hi,
I briefly looked at your code and it would need a bit of effort to make it
compatible with sklearn and the way estimators work. For better usability
it would be great it you could make the solvers be exposed as proper sklearn
estimators i.e. with a fit, predict and coef_. It you do this it mea
Dear, sklearn community,
1. The source code of the Block SBL algorithm is now available at bitbucket:
https://bitbucket.org/liubenyuan/pybsbl
any suggestion, optimization and test on the code are all welcome! as well
as your success stories on applying our methods.
2. Block-OMP is an extension to
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,
Am 28.11.2012 12:00, schrieb Olivier Grisel:
> Also the pending patent of BSBL-BO applied to EEG decoding makes me a
> lot less interested in working on maintaining an open-source version
> of such method knowing that it could not be used without licensing the
> patent in the U.S.
>
> http://techtr
Also the pending patent of BSBL-BO applied to EEG decoding makes me a
lot less interested in working on maintaining an open-source version
of such method knowing that it could not be used without licensing the
patent in the U.S.
http://techtransfer.universityofcalifornia.edu/NCD/22688.html
--
2012/11/28 :
> 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/BSB
Hi,
There is the orthogonal matching pursuit algorithm (an another CS
algorithm) in scikit-learn which is classified as a regression model.
The notations are a bit different from the CS community.
Arnaud Joly
Le 28/11/2012 08:38, [email protected] a écrit :
Hi Meng:
It is a compressive se
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
Hi Meng:
It is a compressive sensing method. Which serves as reconstruct signal from
indeterminacy sensing data:
Y = Phi * x + n
Where Phi is a N*M matrix with N much less than M.
Sorry for my English.
:)
Liu benyuan
在 2012-11-28，15:13，xinfan meng 写道：
> You can first take a look at this pa
Hi Leon
Yes, it is open access, We will put it in one of the authors, zhilin Zhang's
homepage. I will announce once it is available .
It is of great help if you could test it! I will learn git first and fork
sklearn to my home folder.
Best wishes !
Liu benyuan
在 2012-11-28，15:11，Leon Palaf
You can first take a look at this page:
http://scikit-learn.org/stable/developers/index.html .
As to the model, is it a classification / clustering / factorization
algorithm or something?
On Wed, Nov 28, 2012 at 3:08 PM, wrote:
> Dear scikit-learn community:
>
>
> Block Sparse Bayesian Learnin
Hey Liu.
You can probably fork it in github and submit your code as a module in
sklearn, then you would have a healthy test period to finally put it in the
latest release.
If you have it on open access, I would be happy to test it and help you
with the methods, specially because most of the metho
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 imp
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