Corpus ID: 17540942

Gradient-based kernel method for feature extraction and variable selection

@inproceedings{Fukumizu2012GradientbasedKM,
  title={Gradient-based kernel method for feature extraction and variable selection},
  author={Kenji Fukumizu and Chenlei Leng},
  booktitle={NIPS},
  year={2012}
}
  • Kenji Fukumizu, Chenlei Leng
  • Published in NIPS 2012
  • Computer Science
  • We propose a novel kernel approach to dimension reduction for supervised learning: feature extraction and variable selection; the former constructs a small number of features from predictors, and the latter finds a subset of predictors. First, a method of linear feature extraction is proposed using the gradient of regression function, based on the recent development of the kernel method. In comparison with other existing methods, the proposed one has wide applicability without strong… CONTINUE READING

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