Matrix perturbation bounds via contour bootstrapping

Matrix perturbation bounds play an essential role in the design and analysis of spectral algorithms. In this paper, we introduce a new method to deduce matrix perturbation bounds, which we call "contour bootstrapping". As applications, we work out several new bounds for eigensubspace computation and low rank approximation. Next, we use these bounds to study utility problems in the area of differential privacy.