Applying SVD throws a Memory Error instantaneously?

Question

I am trying to apply SVD on my matrix (3241 x 12596) that was obtained after some text processing (with the ultimate goal of performing Latent Semantic Analysis) and I am un

Answer 1

Apparently, as it turns out, thanks to @Ferdinand Beyer, I did not notice that I was using a 32-bit version of Python on my 64-bit machine.

Using a 64-bit version of Python and reinstalling all the libraries solved the problem.

Answer 2

Yes, the full_matrices parameter to scipy.linalg.svd is important: your input is highly rank-deficient (rank max 3,241), so you don't want to allocate the entire 12,596 x 12,596 matrix for V!

More importantly, matrices coming from text processing are likely very sparse. The scipy.linalg.svd is dense and doesn't offer truncated SVD, which results in a) tragic performance and b) lots of wasted memory.

Have a look at the sparseSVD package from PyPI, which works over sparse input and you can ask for top K factors only. Or try scipy.sparse.linalg.svd, though that's not as efficient and only available in newer versions of scipy.

Or, to avoid the gritty details completely, use a package that does efficient LSA for you transparently, such as gensim.