Matrix completion is one of the crucial tools in modern data science research. Recently, a novel sampling model for matrix completion coined cross-concentrated sampling (CCS) has caught much attention. However, the robustness of the CCS model against sparse outliers remains unclear in the existing studies. In this paper, we aim to answer this question by exploring a novel Robust CCS Completion problem. A highly efficient non-convex iterative algorithm, dubbed Robust CUR Completion (RCURC), is proposed. The empirical performance of the proposed algorithm, in terms of both efficiency and robustness, is verified in synthetic and real datasets.

翻译：矩阵补全是现代数据科学研究中的关键工具之一。近年来，一种名为交叉集中采样（CCS）的新型矩阵补全采样模型引起了广泛关注。然而，现有研究中尚不清楚CCS模型对稀疏异常值的鲁棒性。本文旨在通过探索新型鲁棒CCS补全问题来回答这一问题。我们提出了一种高效的非凸迭代算法，称为鲁棒CUR补全（RCURC）。通过合成数据集与真实数据集验证了所提算法在效率与鲁棒性方面的实证性能。