Hi Jaidev,

This seems relevant to your question:

http://metaoptimize.com/qa/questions/7897/are-lasso-and-basis-pursuit-really-the-same-thing

Vlad

On Oct 5, 2012, at 01:16 , Jaidev Deshpande <[email protected]> wrote:

> Hi,
> 
> I've been going through the tomography reconstruction example
> (http://scikit-learn.org/dev/auto_examples/applications/plot_tomography_l1_reconstruction.html).
> If I understand correctly, the desirable solution in a compressed
> sensing problem is one that minimizes the L1 norm. In this example,
> the Lasso estimator seems to be doing that. So, is L1 penalization
> equivalent to L1 minimization?
> 
> Intuitively, I think not. Since Lasso is a gradient descent estimator,
> minimizing the weights (in the L1 sense) would probably underfit the
> model. Thus, what it does for machine learning isn't favourable for
> compressed sensing, and vice versa. I think it worked in this
> particular example because the original image was a simple one, and a
> little penalization might have provided a good estimate of
> minimization of L1.
> 
> Am I right? Is it recommended to use Lasso for a general compressed
> sensing problem? (I should probably experiment with the L1
> minimization with scipy.optimize and see how it behaves against
> Lasso.)
> 
> Thanks
> 
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------------------
Vlad N.
http://vene.ro





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