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Annals of Operations Research

, Volume 263, Issue 1–2, pp 69–91 | Cite as

Application of kernel principal component analysis to multi-characteristic parameter design problems

  • Woojin Soh
  • Heeyoung Kim
  • Bong-Jin Yum
Data Mining and Analytics

Abstract

The Taguchi method for robust parameter design traditionally deals with single characteristic parameter design problems. Extending the Taguchi method to the case of multi-characteristic parameter design (MCPD) problems requires an overall evaluation of multiple characteristics, for which the principal component analysis (PCA) has been frequently used. However, since the PCA is based on a linear transformation, it may not be effectively used for the data with complicated nonlinear structures. This paper develops a kernel PCA-based method that allows capturing nonlinear relationships among multiple characteristics in constructing a single aggregate performance measure. Applications of the proposed method to simulated and real experimental data show the advantages of the kernel PCA over the original PCA for solving MCPD problems.

Keywords

Kernel principal component analysis Multiple performance characteristics Robust parameter design SN ratio Taguchi method 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

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