I mentioned in my original post that I am going to show results of comparing PCA/SVD, NNMF, and ICA over image de-noising. This blog post does that : "Comparison of PCA, NNMF, and ICA over image de-noising".

Below are given the most important comparison images.

Visual comparison of de-noised images

Next we visualize the ICA de-noised images together with the original images and the SVD and NNMF de-noised ones.

There are several ways to present that combination of images.

Comparison using classifiers

We can further compare the de-noising results by building digit classifiers and running them over the de-noised images.

We can see that with ICA we get better results than with PCA/SVD, probably not as good as NNMF, but very close.

BlockRandom[SeedRandom[ToString[First[data]];] -- This is a cool command.

"[...] to seed the random algorithm and still keep it deterministic." -- Hmm... it depends how much luck is needed to get the right results.

Yes, that's what I meant