A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound
Machine Learning
2012-10-05 v1 Discrete Mathematics
Machine Learning
Abstract
The CUR matrix decomposition is an important extension of Nystr\"{o}m 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.
Cite
@article{arxiv.1210.1461,
title = {A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound},
author = {Shusen Wang and Zhihua Zhang and Jian Li},
journal= {arXiv preprint arXiv:1210.1461},
year = {2012}
}
Comments
accepted by NIPS 2012