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In this paper, we address the column-based low-rank matrix approximation problem using a novel parallel approach. Our approach is based on the divide-and-combine idea. We first perform column selection on sub-matrices of an original data matrix in parallel, and then combine the selected columns into the final output. Our approach enjoys a theoretical(More)
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