Unperturbed: spectral analysis beyond Davis-Kahan


Justin Eldridge, Mikhail Belkin, Yusu Wang ;
Proceedings of Algorithmic Learning Theory, PMLR 83:321-358, 2018.


Classical matrix perturbation results, such as Weyl’s theorem for eigenvalues and the Davis-Kahan theorem for eigenvectors, are general purpose. These classical bounds are tight in the worst case, but in many settings sub-optimal in the typical case. In this paper, we present perturbation bounds which consider the nature of the perturbation and its interaction with the unperturbed structure in order to obtain significant improvements over the classical theory in many scenarios, such as when the perturbation is random. We demonstrate the utility of these new results by analyzing perturbations in the stochastic blockmodel where we derive much tighter bounds than provided by the classical theory.

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