A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Shusen Wang, Zhihua Zhang


The CUR matrix decomposition is an important extension of Nyström approximation to a general matrix. It approximates any data matrix in terms of a small number of its columns and rows. In this paper we propose a novel randomized CUR algorithm with an expected relative-error bound. The proposed algorithm has the advantages over the existing relative-error CUR algorithms that it possesses tighter theoretical bound and lower time complexity, and that it can avoid maintaining the whole data matrix in main memory. Finally, experiments on several real-world datasets demonstrate significant improvement over the existing relative-error algorithms.