Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond

@article{Liu2021RandomFF,
  title={Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond},
  author={F. Liu and Xiaolin Huang and Yudong Chen and J. Suykens},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2021},
  volume={PP}
}
  • F. Liu, Xiaolin Huang, +1 author J. Suykens
  • Published 2021
  • Medicine, Mathematics, Computer Science
  • IEEE transactions on pattern analysis and machine intelligence
Random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. The body of work on random features has grown rapidly, and hence it is desirable to have a comprehensive overview on this topic explaining the connections among various algorithms and theoretical results. In this survey, we systematically review the work on random features… Expand
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