Ive implemented the classical MultiDimensional Scaling for the scikit learn using both functions. Their behavior surprised me for "big" arrays (10000 by 10000, symmetric as it is a similarity matrix).

linalg.svd() raises a memory error because it tries to allocate a (7000000,) array (in fact bigger than that !). This is strange because the test was made on a 64bits Linux, so memory should not have been a problem.

linalg.eigh() fails to diagonalize the matrix, it gives me NaN as a result, and this is not very useful.

A direct optimization of the underlying cost function can give me an adequate solution.

I cannot attach the matrix file (more than 700MB when pickled), but if anyone has a clue, I'll be glad.

Matthieu

--

French PhD student

Website : http://matthieu-brucher.developpez.com/

Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92

LinkedIn : http://www.linkedin.com/in/matthieubrucher