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

Feng Zhao, Islem Rekik, Seong Whan Lee, Jing Liu, Junying Zhang, Dinggang Shen, Jose Garcia-Rodriguez

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Article number5937274
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported in part by National Natural Science Foundation of China (Grants nos. 61773244, 61373079, and 61572344), National Institutes of Health in USA (AG041721, MH107815, EB006733, EB008374, and EB009634), and Provincial Natural Science Foundation of Shanxi in China (2018JM4018).

Publisher Copyright:
© 2019 Feng Zhao et al.

ASJC Scopus subject areas

  • General Computer Science
  • General


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