thanks yarik,<br><br><div class="gmail_quote"><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
What specific use for those did you have in mind?<br></blockquote><div><br>very often in neuroimaging studies, you derive multiple features from the same place (voxel or vertex). it would be interesting to see if the combination of features from a given location worked better compared to treating each of these features independently.<br>
<br>cheers,<br><br>satra<br> </div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div><div></div><div class="h5">On Thu, 06 May 2010, Satrajit Ghosh wrote:<br>
<br>
> hi,<br>
> i have a set of features that share geometrical/spatial properties. is<br>
> there a way i can tell pymvpa that these features are tied together?<br>
> for example, i might get curvature and cortical thickness from a single<br>
> vertex as features, but currently i don't encode the fact that these<br>
> two features are related. the ds is just a nsubj x nfeat matrix.<br>
> and related to this question, are there specific classifiers that can<br>
> take advantage of this relation?<br>
> and to extend it beyond simple grouping based on location, can i<br>
> include neighborhood relations on surfaces or to generalize<br>
> (connectivity on graphs).<br>
> cheers,<br>
> satra<br>
<br>
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