Apologies. Sent to the wrong list.
On Fri, Apr 6, 2012 at 8:18 AM, Anthony Bak <[email protected]> wrote: > I'd like to use isomap (and other manifold learning techniques) with > abstract metric spaces (and perhaps more generally similarity and > dissimilarity matricies - but we can put that aside for the moment). > It looks to me like isomap assumes points are described by points in > R^N or some data structure (such as a KD-Tree) built from such points. > > Q1: Can I use the version of isomap in sklearn with abstract metric spaces? > > I assumed that I could not based on a quick reading of the > documentation six months or so ago and I wrote a pure python > implementation (Based on the original Tenenbaum Matlab > implementation). > > Q2: If the answer to Q1 is "no", how do I go about getting this more > general isomap into the sklearn code? > > Do I need to make a case for handeling non-embedded data or are the > advantages obvious to everyone? > > Thanks. _______________________________________________ macports-users mailing list [email protected] http://lists.macosforge.org/mailman/listinfo.cgi/macports-users
