WH=V matrix decomposition that allows negative values

I'm looking for a matrix factorization method that is able to decompose a matrix:

V => W * H
• V has dimensions m*n
• W has dimensions m*k
• H has dimensions k*n
• V, W, and H can have negative values.
• comes with an implementation in either R, Python, or C/++

I.e., something like Non-negative matrix factorization that allows negative values.

Does anyone have a hint?

• Have you considered singular value decomposition? If $\Sigma$ has $k$ or fewer non-zero singular values, you can compute a $W$ and $H$ direct from the SVD, such as $W=U\Sigma^\frac{1}{2}$ and $H=\Sigma^\frac{1}{2}V^*$ – Glen_b Sep 14 '14 at 17:16
• Thanks for the hint, I thought that one possible answer would be some variant of SVD. Unfortunately my linear algebra is so rusty that I didn't see it myself - if you add this as an answer I will accept it. – Michael Schubert Sep 17 '14 at 20:02

If $\Sigma$ has $k$ or fewer non-zero singular values, you can compute a $W$ and $H$ direct from the SVD, such as $W=U\Sigma^\frac{1}{2}$ and $H=\Sigma^\frac{1}{2}V^∗$.
With the diagonal of $\Sigma$ ordered such that the zeroes are all at the later diagonals, columns of $W$ after the $k$-th will be all-zero (and similarly columns of $H$) and can be dropped without changing the product.