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
