Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd Edition, Academic Press, Inc., New York, (1990)zbMATHGoogle Scholar
  2. [2]
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, China Machine Press, Beijing, (2004)zbMATHGoogle Scholar
  3. [3]
    Kirby, M., Sirovich, L.: Application of the KL Procedure for the Characterization of Human Faces, IEEE Trans. Pattern Anal. Machine Intell. 12(1) (1990) 103–108CrossRefGoogle Scholar
  4. [4]
    Turk, M., Pentland, A.: Face Recognition Using Eigenfaces, Proc. IEEE Conf. On Computer Vision and Pattern Recognition, (1991)586–591Google Scholar
  5. [5]
    Yang, J., Yang, J.-Y.: Why can LDA be Performed in PCA Transformed Space? Pattern Recognition 36(2) (2003) 563–566CrossRefGoogle Scholar
  6. [6]
    Liu, C.: Gabor-based Kernel PCA with Fractional Power Polynomial Models for Face Recognition, IEEE Trans. Pattern Analysis and Machine Intelligence 26(5) (2004) 572–581CrossRefGoogle Scholar
  7. [7]
    Bian, Z., Zhang, X.: Pattern Recognition (in Chinese), Tsinghua University Press, Beijing, (2000)Google Scholar
  8. [8]
    Jin, Z., Davoine, F., Lou, Z., Yang, J.-Y.: A Novel PCA-based Bayes Classifier and Face Analysis, IAPR International Conference on Biometrics (ICB2006), Hong Kong, Jan. 5–7, (2006)Google Scholar
  9. [9]
    Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation 10(5) (1998) 1299–1319CrossRefGoogle Scholar
  10. [10]
    Scholkopf, B., Smola, A., Muller, K.R.: Kernel Principal Component Analysis, Artificial Neural Networks-ICANN’97, Berlin, (1997) 583–588Google Scholar
  11. [11]
    Xu, Y., Yang, J.-Y., Lu, J., Yu, D.-J.: An Efficient Renovation on Kernel Fisher Discriminant Analysis and Face Recognition Experiments, Pattern Recognition 37 (2004) 2091–2094CrossRefGoogle Scholar
  12. [12]
    Xu, Y.,. Yang, J.-Y, Yang, J.: A Reformative Kernel Fisher Discriminant Analysis, Pattern Recognition 37 (2004) 1299–1302zbMATHCrossRefGoogle Scholar
  13. [13]
    Xu, Y., Yang, J.-Y., Lu, J.-F.: An Efficient Kernel-based Nonlinear Regression Method for Two-class Classification, Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, August, (2005) 4442–4445Google Scholar
  14. [14]
    Xu, Y., Zhang, D., Jin, Z., Li, M., Yang, J.-Y.: A Fast Kernel-based Nonlinear Discriminant Analysis for Multi-class Classification, Pattern Recognition, (2006) 39(6), 1026–1033CrossRefGoogle Scholar

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

Personalised recommendations