Linear Algebra, Randomization, Singular Values, Eigenvectors, Thoughts And Theory.
to perform QR decomposition on T instead of S! Note that, now we have the orthonormal bases for the r-rank approximation of S. Now we take another projection of S, but this time on the subspace spanned by the orthonormal matrix Q. One of the serious advantage offered by SVD is, it is generalized