  At sublinear computational cost we greatly decrease the spectral error norm
of a crude Low Rank Approximation LRA) of a matrix that has fast decaying
spectrum of singular values. Our formal estimates for this decrease is in good
accordance with our numerical tests for synthetic and real-world inputs.
Moreover, empirically, for a large subclass of inputs with fast decaying
spectra, the entire LRA with our escalation was accurate and had sublinear
cost.
