Invertibility of random submatrices via tail-decoupling and a matrix Chernoff inequality

Let X be a n×p real matrix with coherence μ(X)=maxj≠j′|XjtXj′|. We present a simplified and improved study of the quasi-isometry property for most submatrices of X obtained by uniform column sampling. Our results depend on μ(X), the operator norm ‖X‖ and the dimensions with explicit constants, which improve the previously known values by a large factor. The analysis relies on a tail-decoupling argument, of independent interest, and a recent version of the Non-Commutative Chernoff inequality (NCCI).