Corpus ID: 6161569

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions

@article{Wang2016SPSDMA,
  title={SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions},
  author={Shusen Wang and Luo Luo and Z. Zhang},
  journal={J. Mach. Learn. Res.},
  year={2016},
  volume={17},
  pages={49:1-49:49}
}

Shusen Wang, Luo Luo, Z. Zhang

Published 2016

Computer Science, Mathematics

J. Mach. Learn. Res.

Symmetric positive semidefinite (SPSD) matrix approximation is an important problem with applications in kernel methods. However, existing SPSD matrix approximation methods such as the Nystrom method only have weak error bounds. In this paper we conduct in-depth studies of an SPSD matrix approximation model and establish strong relative-error bounds. We call it the prototype model for it has more efficient and effective extensions, and some of its extensions have high scalability. Though the… CONTINUE READING

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