Advertisement

A KPCA and DEA Model for Region Innovation Efficiency

  • Xuanli Lv
Conference paper
  • 1.7k Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 143)

Abstract

In recent years, evaluating the region innovation activity has gained a renewed interest in both growth economists and trade economists. In this work, a two-stage architecture constructed by combining kernel principal component analysis (KPCA) and the data envelopment analysis (DEA) is proposed for evolution region innovation. In the first stage, KPCA is used as feature extraction. In the second stage, DEA is used to evolution region innovation efficiency. By examining the region innovation data, it is shown that the proposed method achieves is effective and feasible. And it provides a better estimate tool for the region innovation activity. It also provides a novel way for the evolution design of the other engineering.

Keywords

kernel principal component analysis features selection data envelopment analysis Evolution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Imoto, S., Yabuuchi, Y., Watada, J.: Fuzzy regression model of R&D project evaluation. Applied Soft Computing 8(3), 1266–1273 (2008)CrossRefGoogle Scholar
  2. 2.
    Cameron, G., Proudman, J., Redding, S.: Technological convergence, R&D, trade and productivity Growth. European Economic Review 79, 775–807 (2005)CrossRefGoogle Scholar
  3. 3.
    Huang, S.H., Ke, H.R., Yang, W.P.: Structure clustering for Chinese patent documents. Expert Systems with Applications 34(4), 2290–2297 (2008)CrossRefGoogle Scholar
  4. 4.
    Chen, C.M.: A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks. European Journal of Operational Research 194(3), 687–699 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Fu, Y.w., Yin, H., Yang, G.B.: Application of a mixed DEA model to evaluate relative efficiency validity. Journal of Marine Science and Application 4(3), 64–70Google Scholar
  6. 6.
    Rosipal, R., Girolami, M., Trejo, L.J., Cichocki, A.: Kernel PCA for Feature Extraction and De-noising in Non-linear Regression. Neural Computing & Applications 10(3), 231–243 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Li, Z., Tian, X.M.: Study of Soft Sensor Modeling Method Based on KPCA-SVM. Intelligent Control and Automation, 4876 –4880 (2006)Google Scholar
  8. 8.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Nonlinear feature selection with the Foundations and Applications in Feature extraction. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xuanli Lv
    • 1
  1. 1.School of managementHefei university of technologyChina

Personalised recommendations