# Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion

@article{Ma2020AdversarialCT, title={Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion}, author={Qianqian Ma and Alexander Olshevsky}, journal={ArXiv}, year={2020}, volume={abs/2010.12181} }

We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknown and can be arbitrarily large. It is not known which revealed entries are corrupted. We propose a new algorithm combining alternating minimization with extreme-value filtering and provide sufficient and necessary conditions to recover the original rank-one matrix. In particular, we show that our proposed algorithm is…

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