Efficient KPCA-Based Feature Extraction: A Novel Algorithm and Experiments

  • Yong Xu
  • David Zhang
  • Jing-Yu Yang
  • Zhong Jing
  • Miao Li
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)


KPCA has been widely used for feature extraction. It is noticeable that the efficiency of KPCA-based feature extraction is in inverse proportion to the size of the training sample set. In order to speed up KPCA-based feature extraction, we develop a novel algorithm(i.e. IKPCA) which improves KPCA with a distinctive viewpoint. The algorithm is methodologically consistent with KPCA with clear physical meaning. Experiments on several benchmark datasets illustrate that IKPCA-based feature extraction is much faster than KPCA-based feature extraction. The ratio of IKPCA-based feature extraction time to KPCA-based feature extraction time may be smaller than 0.30. Furthermore, the classification accuracy corresponding to IKPCA is comparable with KPCA.


Principal Component Analysis Feature Extraction Feature Space Training Sample Kernel Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yong Xu
    • 1
    • 2
  • David Zhang
    • 3
  • Jing-Yu Yang
    • 1
  • Zhong Jing
    • 1
  • Miao Li
    • 2
  1. 1.Department of Computer Science & TechnologyNanjing University of Science & TechnologyNanjingChina
  2. 2.Bio-Computing Research Center and Shenzhen graduate schoolHarbin Institute of TechnologyShenzhenChina
  3. 3.The Biometrics Research Center and Department of ComputingHong Kong Polytechnic UniversityKowloonHong Kong

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