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

Author

Listed:
  • 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))

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.

Suggested Citation

  • Woojin Soh & Heeyoung Kim & Bong-Jin Yum, 2018. "Application of kernel principal component analysis to multi-characteristic parameter design problems," Annals of Operations Research, Springer, vol. 263(1), pages 69-91, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-015-1889-2
    DOI: 10.1007/s10479-015-1889-2
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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    Cited by:

    1. Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
    2. Ma, Mina & Li, Xiaoyu & Gao, Wei & Sun, Jinhua & Wang, Qingsong & Mi, Chris, 2022. "Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA," Applied Energy, Elsevier, vol. 324(C).
    3. Guangqi Liang & Dongxiao Niu & Yi Liang, 2020. "Core Competitiveness Evaluation of Clean Energy Incubators Based on Matter-Element Extension Combined with TOPSIS and KPCA-NSGA-II-LSSVM," Sustainability, MDPI, vol. 12(22), pages 1-26, November.
    4. Feng Zhao & Islem Rekik & Seong-Whan Lee & Jing Liu & Junying Zhang & Dinggang Shen, 2019. "Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets," Complexity, Hindawi, vol. 2019, pages 1-17, February.

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