Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2005: Advances in Neural Networks – ISNN 2005 pp 1028–1033Cite as

A Modified MCA EXIN Algorithm and Its Convergence Analysis

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

Abstract

The minor component is the eigenvector associated with the smallest eigenvalue of the covariance matrix of the input data. The minor component analysis (MCA) is a statistical method for extracting the minor component. Many neural networks have been proposed to solve MCA. However, there exists the problem of the divergence of the norm of the weight vector in these neural networks. In this paper, a modification to the well known MCA EXIN algorithm is presented by adjusting the learning rate. The modified MCA EXIN algorithm can guarantee that the norm of the weight vector of the neural network converges to a constant. Mathematical proofs and simulation results are given to show the convergence of the algorithm.

This work was supported by National Science Foundation of China under Grant 60471055 and Specialized Research Fund for the Doctoral Program of Higher Education under Grant 20040614017.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gao, K., Ahmad, M.O., Swamy, M.N.: Learning Algorithm for Total Least Squares Adaptive Signal Processing. Electron. Lett. 28, 430–432 (1992)

    Article  Google Scholar 

  2. Xu, L., Oja, E., Suen, C.: Modified Hebbian Learning for Curve and Surface Fitting. Neural Networks 5, 441–457 (1992)

    Article  Google Scholar 

  3. Fiori, S., Piazza, F.: Neural MCA for Robust Beamforming. In: Proc of International Symposium on circuits and Systems (ISCAS 2000), vol. III, pp. 614–617 (2000)

    Google Scholar 

  4. Cirrincione, M., Cirrincione, G., Herault, J., Huffel, S.V.: The MCA EXIN Neuron for the Minor Component Analysis. IEEE Trans. Neural Networks 13, 160–187 (2002)

    Article  Google Scholar 

  5. Taleb, A., Cirrincione, G.: Against the Convergence of the Minor Component Analysis Neurons. IEEE Trans. Neural Networks 10, 207–210 (1999)

    Article  Google Scholar 

  6. Zufiria, P., On, J.: the Discrete-time Dynamics of the Basic Hebbian Neural-network Nodes. IEEE Trans. Neural Networks 13, 1342–1352 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Dezhong Peng, Zhang Yi & XiaoLin Xiang

Authors
  1. Dezhong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. XiaoLin Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Peng, D., Yi, Z., Xiang, X. (2005). A Modified MCA EXIN Algorithm and Its Convergence Analysis. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_165

Download citation

  • DOI: https://doi.org/10.1007/11427391_165

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation