(WIP) Sparse models: S-LASSO and TV-l1

- Supports TV-l1 and S-LASSO priors
- Supports logistic and squared losses
- Has cross validation
- Can automatically select alpha by CV (+ automatic computation of useful alpha ranges for the CV)
- Warning: User must supply l1_ratio
Both views are certainly reasonable and just as *ad hoc* as one another. I
think there is room for this to evolve in future, and we should leave it at
that for now (and see how it goes). BTW,  issue #265 (
https://github.com/nilearn/nilearn/issues/265) was created to track this
discussion.

On Mon, Nov 3, 2014 at 12:52 PM, eickenberg <notifications@github.com>
wrote:

> In nilearn/decoding/space_net.py:
>
> > +        documentation for details
> > +
> > +    high_pass : False or float, optional (default None)
> > +        This parameter is passed to signal. Clean. Please see the related
> > +        documentation for details
> > +
> > +    t_r : float, optional (default None)
> > +        This parameter is passed to signal.clean. Please see the related
> > +        documentation for details
> > +
> > +    screening_percentile : float in the interval [0, 100]; Optional (
> > +    default 20)
> > +        Percentile value for ANOVA univariate feature selection. A value of
> > +        100 means 'keep all features'. This percentile is is expressed
> > +        w.r.t the volume of a standard (MNI152) brain, and so is corrected
> > +        at runtime by premultiplying it with the ratio of the volume of the
>
> Maybe I'm wrong, but that basically excludes all ROI based analyses from
> benefitting from sensible out-of-the-box settings, right? (I am not
> mentioning single subject analyses as an argument, but one may want to
> accommodate those as well in the future.) As @bthirion
> <https://github.com/bthirion> says, a *volume* percentile of a specified
> mask seems as sensible to me as selecting a percentile can get in the
> context of this algorithm.
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/nilearn/nilearn/pull/219/files#r19728797>.
>



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