Open Access
2013 Kernel Sliced Inverse Regression: Regularization and Consistency
Qiang Wu, Feng Liang, Sayan Mukherjee
Abstr. Appl. Anal. 2013(SI32): 1-11 (2013). DOI: 10.1155/2013/540725


Kernel sliced inverse regression (KSIR) is a natural framework for nonlinear dimension reduction using the mapping induced by kernels. However, there are numeric, algorithmic, and conceptual subtleties in making the method robust and consistent. We apply two types of regularization in this framework to address computational stability and generalization performance. We also provide an interpretation of the algorithm and prove consistency. The utility of this approach is illustrated on simulated and real data.


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Qiang Wu. Feng Liang. Sayan Mukherjee. "Kernel Sliced Inverse Regression: Regularization and Consistency." Abstr. Appl. Anal. 2013 (SI32) 1 - 11, 2013.


Published: 2013
First available in Project Euclid: 26 February 2014

zbMATH: 1364.62101
MathSciNet: MR3081598
Digital Object Identifier: 10.1155/2013/540725

Rights: Copyright © 2013 Hindawi

Vol.2013 • No. SI32 • 2013
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