Data-driven Random Fourier Features using Stein Effect

@inproceedings{Chang2017DatadrivenRF,
  title={Data-driven Random Fourier Features using Stein Effect},
  author={Wei-Cheng Chang and Chun-Liang Li and Yiming Yang and Barnab{\'a}s P{\'o}czos},
  booktitle={IJCAI},
  year={2017}
}
Large-scale kernel approximation is an important problem in machine learning research. Approaches using random Fourier features have become increasingly popular [Rahimi and Recht, 2007], where kernel approximation is treated as empirical mean estimation via Monte Carlo (MC) or Quasi-Monte Carlo (QMC) integration [Yang et al., 2014]. A limitation of the current approaches is that all the features receive an equal weight summing to 1. In this paper, we propose a novel shrinkage estimator from… CONTINUE READING
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