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

Woojin Soh (), Heeyoung Kim () and Bong-Jin Yum ()
Additional contact information
Woojin Soh: Korea Advanced Institute of Science and Technology (KAIST)
Heeyoung Kim: Korea Advanced Institute of Science and Technology (KAIST)
Bong-Jin Yum: Korea Advanced Institute of Science and Technology (KAIST)

Annals of Operations Research, 2018, vol. 263, issue 1, No 5, 69-91

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-015-1889-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-015-1889-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-015-1889-2

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().