A randomized approach to efficient kernel clustering

@article{Anaraki2016ARA,
  title={A randomized approach to efficient kernel clustering},
  author={Farhad Pourkamali Anaraki and Stephen Becker},
  journal={2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)},
  year={2016},
  pages={207-211}
}
  • Farhad Pourkamali Anaraki, Stephen Becker
  • Published 2016
  • Mathematics, Computer Science
  • 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
  • Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data. A dominant concern is the memory requirement since memory scales as the square of the number of data points. We provide a new analysis of a class of approximate kernel methods that have more modest memory requirements, and propose a specific one-pass randomized kernel approximation followed by standard K-means on the transformed data. The analysis and… CONTINUE READING

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