On the Adversarial Robustness of Subspace Learning
On the Adversarial Robustness of Subspace Learning
In this paper, we investigate the adversarial robustness of subspace learning problems. Different from the scenario addressed by classic robust algorithms that assume fractions of data are corrupted, we consider a more powerful adversary who can observe the whole data and modify all of them. The goal of the adversary …