SVD vs matched filter
That's not true, it's not better.
The thing is: the matched filter just implements the projection in the signal vector space, onto the signal vector itself (or a multiple thereof). (You'll find correlation is just an inner product in that space.)
The line through that vector is the signal subspace, the plane to which that vector is normal is the noise space. Matched filtering is a method of linear algebra, and not different than using the SVD to find the subspaces. It's just that for matched filtering, the signal space is known.