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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper addresses the problem of robustly estimating the low-dimensional subspace of contaminated observations when the observations are inherently coherent. Performance goes worse with increasing data coherence is a standard theoretical bottleneck of previous RPCA methods. This paper, however, circumvents this problem in a clever manner. Considering that such cluster structure is rather common in realistic data, solving this issue is certainly significantly meaningful.