hi yarik,
hm... interesting -- and there is no comparison against "minimizing
> independence"? e.g. dCov measure
> http://en.wikipedia.org/wiki/Distance_correlation which is really simple
> to estimate and as intuitive as a correlation coefficient
>
thanks for bringing up dCov. have you had a chance to play with it (their R
package) [1] /try to implement it from their paper [2]. it seems like dCov,
from the paper doesn't apply to 2 vectors, but it can compare the
covariance of two datasets, where n_samples>1. their code also allows you
to enter two distance matrices into dCor and dCov and dCor(x,x) != dCor(x).
i would love if somebody can explain to me how dCor, dCov can be used for 2
random vectors.
all that said, i started implementing dCorr/dCov for sklearn and stopped as
i couldn't match my output with the R code (i didn't actually look at the
source there) and it seemed it wasn't going to be related to comparing two
samples.
cheers,
satra
[1] http://cran.r-project.org/web/packages/energy/index.html
[2] http://arxiv.org/pdf/0803.4101.pdf
>
> On Fri, 15 Jun 2012, Satrajit Ghosh wrote:
>
> > fyi
> > ---------- Forwarded message ----------
> > From: joshua vogelstein <[1][email protected]>
> > Date: Fri, Jun 15, 2012 at 12:35 AM
>
> > [2]http://jmlr.csail.mit.edu/papers/volume13/song12a/song12a.pdf
> > these guys define a nice nonlinear/nonparametric measure of
> correlation
> > that might be of interest to you.
>
> --
> Yaroslav O. Halchenko
> Postdoctoral Fellow, Department of Psychological and Brain Sciences
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
> WWW: http://www.linkedin.com/in/yarik
>
>
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