Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression

@article{Yeh2009NonlinearDR,
  title={Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression},
  author={Yi-Ren Yeh and Su-Yun Huang and Yuh-Jye Lee},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2009},
  volume={21},
  pages={1590-1603}
}
Sliced inverse regression (SIR) is a renowned dimension reduction method for finding an effective low-dimensional linear subspace. Like many other linear methods, SIR can be extended to nonlinear setting via the ldquokernel trick.rdquo The main purpose of this paper is two-fold. We build kernel SIR in a reproducing kernel Hilbert space rigorously for a more intuitive model explanation and theoretical development. The second focus is on the implementation algorithm of kernel SIR for fast… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 30 references

Kernel sliced inverse regression with applications on classification

  • H. M. Wu
  • Journal of Computational and Graphical Statistics…
  • 2008
Highly Influential
5 Excerpts

UCI repository of machine learning databases. http://www.ics.uci.edu/∼mlearn/mlrepository.html

  • A. Asuncion, D. J. Newman
  • 2007.
  • 2007
1 Excerpt

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