Isomap with more general inputs.

[Anthony Bak]

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.