[pymvpa] Fwd: new feature selection algorithm

[Yaroslav Halchenko]

Haven't seen it... from the abstract it sounds really close (if not
identical) to what we do with null_dist in DatasetMeasure... we
just don't have it conveniently exposed as FeatureSelection, since it is
stored in null_prob state variable...

On Tue, 30 Sep 2008, Per B. Sederberg wrote:

> In case y'all didn't see this...
> P


> ---------- Forwarded message ----------
> From: Sam Gershman <sjgershm at princeton.edu>
> Date: Tue, Sep 30, 2008 at 7:43 PM
> Subject: new feature selection algorithm
> To: compmemlist at princeton.edu


> Hi all,

> Some people might be interested in this:
> http://www.pnas.org/content/105/39/14790.full
> It just came out in PNAS. The paper describes an algorithm for feature
> selection called "higher criticism." It is designed for a particular
> classification setting dubbed "rare/weak": where the fraction of
> useful features is small and the useful features are each too weak to
> be useful on their own. The idea is to look at the distribution of
> feature statistics and use the deviation from an expected null
> distribution to set the feature selection threshold. Although based in
> frequentist statistics and therefore fundamentally flawed, it shows
> some interesting behavior that could be highly advantageous to MVPA
> applied to fMRI. Apart from its explicit designation for the R/W
> setting (which is obviously apt for fMRI), it doesn't require tuning
> by cross-validation and the threshold has low variance (which might be
> good when looking for consistent feature sets).

> Sam

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-- 
Yaroslav Halchenko
Research Assistant, Psychology Department, Rutgers-Newark
Student  Ph.D. @ CS Dept. NJIT
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