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arXiv · 2110.13146

Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory

Chen Zhao·Yuqing Liu·Li Hu·Zhengzhong Yuan
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Abstract

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable.

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Chen Zhao, Yuqing Liu, Li Hu, Zhengzhong Yuan. 2022-04-06. Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory. https://doi.org/10.1155/2022%2F5448123

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