Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

@article{Fukumizu2004DimensionalityRF,
  title={Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces},
  author={Kenji Fukumizu and Francis R. Bach and Michael I. Jordan},
  journal={Journal of Machine Learning Research},
  year={2004},
  volume={5},
  pages={73-99}
}
We propose a novel method of dimensionality reduction for supervised learning problems. Given a regression or classification problem in which we wish to predict a response variable Y from an explanatory variable X, we treat the problem of dimensionality reduction as that of finding a low-dimensional “effective subspace” for X which retains the statistical relationship between X and Y . We show that this problem can be formulated in terms of conditional independence. To turn this formulation… CONTINUE READING
Highly Influential
This paper has highly influenced a number of papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 444 citations. REVIEW CITATIONS

10 Figures & Tables

Topics

Statistics

02040'04'06'08'10'12'14'16'18
Citations per Year

444 Citations

Semantic Scholar estimates that this publication has 444 citations based on the available data.

See our FAQ for additional information.