Problem. I have a symmetric distance matrix with entries between zero and one, like this one:
D = ( 0.0 0.4 0.0 0.5 )
( 0.4 0.0 0.2 1.0 )
( 0.0 0.2 0.0 0.7 )
( 0.5 1.0 0.7 0.0 )
I would like to find points in the plane that have (approximately) the pairwise distances given in D. I understand that this will usually not be possible with strictly correct distances, so I would be happy with a "good" approximation.
My matrices are smallish, no more than 10x10, so performance is not an issue.
Question. Does anyone know of an algorithm to do this?
Background. I have sets of probability densities between which I calculate Hellinger distances, which I would like to visualize as above. Each set contains no more than 10 densities (see above), but I have a couple of hundred sets.
What I did so far.
I did consider posting at math.SE, but looking at what gets tagged as "geometry" there, it seems like this kind of computational geometry question would be more on-topic here. If the community thinks this should be migrated, please go ahead.
This looks like a straightforward problem in computational geometry, and I would assume that anyone involved in clustering might be interested in such a visualization, but I haven't been able to google anything.
One simple approach would be to randomly plonk down points and perturb them until the distance matrix is close to D, e.g., using Simulated Annealing, or run a Genetic Algorithm. I have to admit that I haven't tried that yet, hoping for a smarter way.
One specific operationalization of a "good" approximation in the sense above is Problem 4 in the Open Problems section here, with k=2. Now, while finding an algorithm that is guaranteed to find the minimum l1-distance between D and the resulting distance matrix may be an open question, it still seems possible that there at least is some approximation to this optimal solution. If I don't get an answer here, I'll mail the gentleman who posed that problem and ask whether he knows of any approximation algorithm (and post any answer I get to that here).
