Hi,

Unfortunately, statistics is not magic, and they are many situation in
which l1 recovery is not garanteed to work.

I cannot give magic answers, and I suggest that you think a lot about how
you can validate any findings using external sources. That said, I would
suggest, in general, to standardize your variables, and to use
randomized-sparsity, rather than simple l1, for feature selection:
http://scikit-learn.org/stable/modules/feature_selection.html#randomized-sparse-models

G

On Sat, Jun 01, 2013 at 08:22:41AM -0400, o m wrote:
> I've been playing around with Lasso and Lars, but there's something that
> bothers me about standardization.

> If I don't standardize to N(0, 1), these procedures indicate that a certain 
> set
> of variables are the most important. Yet, if I standardize, I get a completely
> different set of variables. As expected, the lars or lasso plots from varying
> alpha look very different. I know there's  a good reason for this, but then
> what's the right way to identify the important variables from a large set?

> I could take prediction quality on testing data, but there's a conflict if the
> important variables are so different under standardization.

> Any help or pointers is appreciated.

> Best Regards.


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-- 
    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

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