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Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets

Author

Listed:
  • Feng Zhao
  • Islem Rekik
  • Seong-Whan Lee
  • Jing Liu
  • Junying Zhang
  • Dinggang Shen

Abstract

As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling massive or online datasets. To overcome this drawback of KPCA, in this paper, we propose a two-phase incremental KPCA (TP-IKPCA) algorithm which can incorporate data into KPCA in an incremental fashion. In the first phase, an incremental algorithm is developed to explicitly express the data in the kernel space. In the second phase, we extend an incremental principal component analysis (IPCA) to estimate the kernel principal components. Extensive experimental results on both synthesized and real datasets showed that the proposed TP-IKPCA produces similar principal components as conventional batch-based KPCA but is computationally faster than KPCA and its several incremental variants. Therefore, our algorithm can be applied to massive or online datasets where the batch method is not available.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:complx:5937274
    DOI: 10.1155/2019/5937274
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    References listed on IDEAS

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    1. Nicole, Sandro, 2000. "Feedforward neural networks for principal components extraction," Computational Statistics & Data Analysis, Elsevier, vol. 33(4), pages 425-437, June.
    2. 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.
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