Model-free Variable Selection in Reproducing Kernel Hilbert Space

@article{Yang2016ModelfreeVS,
  title={Model-free Variable Selection in Reproducing Kernel Hilbert Space},
  author={Lei Yang and Shaogao Lv and Junhui Wang},
  journal={Journal of Machine Learning Research},
  year={2016},
  volume={17},
  pages={82:1-82:24}
}
Variable selection is popular in high-dimensional data analysis to identify the truly informative variables. Many variable selection methods have been developed under various model assumptions. Whereas success has been widely reported in literature, their performances largely depend on validity of the assumed models, such as the linear or additive models. This article introduces a model-free variable selection method via learning the gradient functions. The idea is based on the equivalence… CONTINUE READING
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Highly Influential
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