[pymvpa] Shrinkage LDA for PyMVPA

[Yaroslav Halchenko]

On Tue, 23 Jan 2018, Michael Bannert wrote:

> Dear PyMVPA users,

> Since I couldn't find an implementation of "shrinkage LDA" (e.g., Pereira &
> Botvinick, NeuroImage, 2011) in PyMVPA, I implemented my own based on
> Gaussian Discriminant Analysis (gda.py). I figured it could be helpful for
> other users as well?

> It is considerably faster than using the scikit-learn implementation through
> the SKLLearnerAdapter, which I have been using until now:

> SKLLearnerAdapter(LinearDiscriminantAnalysis(solver='lsqr',
> shrinkage='auto'))

> It is also a little more flexible because you can choose between the
> Ledoit-Wolf estimator and the Oracle Approximating Shrinkage.

> May it be useful.

Sounds great.  Would you mind submitting a PR so your contribution
authorship is properly recorded, and so we could make sure it works
correctly (by running at least the standard batch of tests after it gets
added to the warehouse which I think you have already done)?


-- 
Yaroslav O. Halchenko
Center for Open Neuroscience     http://centerforopenneuroscience.org
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
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