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What are the standard methods of computing the rank-k truncated SVD of large dense matrices? My literature search yields results only for large sparse matrices.

I assume for k small that you use a Krylov subspace method (this is what Matlab's svds does). But (empirically) how large can k get before these methods become impractical, and then what should one resort to?

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Not sure why this had two downvotes... – Yemon Choi Mar 15 '13 at 7:12
Would this question not be more appropriate for – guest Sep 11 '14 at 22:07

When dense matrix is so large that Krylov-based techniques become impractical, one have to rely on certain specific of a matrix to compute its low-rank approximation. Because different matrices have different properties, I doubt there are any standard approaches, which work as a silver bullet.

However, you can try to compute a low-rank approximation of your matrix by a cross interpolation:

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