Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression

  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},
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


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