Abstract:We consider a data corruption scenario in the classical k Nearest Neighbors (k-NN) algorithm, that is, the testing data are randomly perturbed. Under such a scenario, the impact of corruption level on the asymptotic regret is carefully characterized. In particular, our theoretical analysis reveals a phase transition phenomenon that, when the corruption level ω is below a critical order (i.e., small-ω regime), the asymptotic regret remains the same; when it is beyond that order (i.e., large-ω regime), the asymp… Show more
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