Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

  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},
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
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