Isomap with more general inputs.

[Anthony Bak]

Apologies. Sent to the wrong list.

On Fri, Apr 6, 2012 at 8:18 AM, Anthony Bak <anthony.bak at gmail.com> 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.