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Reseach Article

A New Learning Algorithm for Single Hidden Layer Feedforward Neural Networks

by Virendra P. Vishwakarma, M. N. Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 6
Year of Publication: 2011
Authors: Virendra P. Vishwakarma, M. N. Gupta
10.5120/3390-4706

Virendra P. Vishwakarma, M. N. Gupta . A New Learning Algorithm for Single Hidden Layer Feedforward Neural Networks. International Journal of Computer Applications. 28, 6 ( August 2011), 26-33. DOI=10.5120/3390-4706

@article{ 10.5120/3390-4706,
author = { Virendra P. Vishwakarma, M. N. Gupta },
title = { A New Learning Algorithm for Single Hidden Layer Feedforward Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 6 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number6/3390-4706/ },
doi = { 10.5120/3390-4706 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:14:04.324516+05:30
%A Virendra P. Vishwakarma
%A M. N. Gupta
%T A New Learning Algorithm for Single Hidden Layer Feedforward Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 6
%P 26-33
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For high dimensional pattern recognition problems, the learning speed of gradient based training algorithms (back-propagation) is generally very slow. Local minimum, improper learning rate and over-fitting are some of the other issues. Extreme learning machine was proposed as a non-iterative learning algorithm for single-hidden layer feed forward neural network (SLFN) to overcome these issues. The input weight and biases are chosen randomly in ELM which makes the classification system of non-deterministic behavior. In this paper, a new learning algorithm is presented in which the input weights and the hidden layer biases of SLFN are assigned from basis vectors generated by training space. The output weights and biases are decided through simple generalized inverse operation on output matrix of hidden layer. This makes very fast learning speed and better generalization performance in comparison to conventional learning algorithm as well as ELM.

References
  1. R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: a survey,” in Proc. IEEE, vol. 83, no. 5, pp. 705–740, May 1995.
  2. W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey”, ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, Dec 2003.
  3. S. Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2005.
  4. V. P. Vishwakarma, S. Pandey, and M. N. Gupta, “Illumination Normalization under Varying Illumination Conditions using Artificial Neural Network”, in Proc. 3rd International Conference on Advance Computing & Communication Technologies, Nov. 2008, pp. 455-460.
  5. V. P. Vishwakarma, S. Pandey, and M. N. Gupta, “Pose Invariant Face Recognition using Virtual Frontal View Generation,” in Proc. International Conference on Computing, Communication and Networking (ICCCN 2008), Dec. 2008, pp. 1-5.
  6. L. Lu, X. Yuan, and T. Yahagi, “A method of face recognition based on fuzzy clustering and parallel neural networks,” Signal Processing, vol. 86, no. 8, pp. 2026-2039, Aug. 2006.
  7. J. Haddadnia and M. Ahmadi, “ N-feature neural network human face recognition,” Image and Vision Computing, vol. 22, vol. 12, pp. 1071-1082, Oct. 2004.
  8. K. Choi, K. A. Toh, and H. Byun, “A Random Network Ensemble for Face Recognition,” Lecture Notes in Computer Sciences, vol. 5558, pp. 92-101, June 2009.
  9. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face Recognition: A Convolutional Neural Network Approach,” IEEE Trans. Neural Networks, vol. 8, no. 1, pp. 98-113, Jan. 1997.
  10. C. Neubauer “Evaluation of Convolutional Neural Networks for Visual Recognition,” IEEE Trans. Neural Networks, vol. 9, no. 4, pp. 685-696, July 1998.
  11. N. Intrator, D. Reisfeld, and Y. Yeshurun, “Face Recognition using a Hybrid Supervised/Unsupervised Neural Network,” Pattern Recognition Letters, vol. 17, no. 1, pp. 67-76, Jan. 1996.
  12. A. Ghosh, B. U. Shankar and S.K. Meher, “A novel approach to neuro-fuzzy classification,” Neural Networks, vol. 22, pp. 100-109, 2009.
  13. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks,” in Proc. Int. Joint Conf. on Neural Networks (Budapest, Hungary), July 2004, pp. 985-990.
  14. S. Haykin, Neural Networks—A Comprehensive Foundation, 2nd Ed. Prentice Hall, 1999.
  15. M. T. Hagan, H.B. Demuth, and M. Beale, Neural Network Design, Thomson Learning, 2002.
  16. S. Tamura and M. Tateishi, “Capabilities of a Four-Layered Feedforward Neural Network: Four Layers Versus Three,” IEEE Trans. Neural Networks, vol. 8, no. 2, pp. 251-255, Mar. 1997.
  17. H. M. El-Bakry, “A Simple Design for High Speed Normalized Neural Networks Implemented in the Frequency Domain for Pattern Detection,” in Proc. International Joint Conference on Neural Networks, Vancouver, Canada, July 2006, pp. 1317-1324.
  18. T. Kwok and K. A. Smith, “A Noisy Self-Organizing Neural Network With Bifurcation Dynamics for Combinatorial Optimization,” IEEE Trans. Neural Networks, vol. 15, no. 1, pp. 84-98, Jan. 2004.
  19. L. Wang, “On Competitive Learning,” IEEE Trans. Neural Networks, vol. 8, no. 5, pp. 1214-1217, Sep. 1997.
  20. P. L. Bartlett, “The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network,” IEEE Trans. Information Theory, vol. 44, no. 2, pp. 525-536, Mar. 1998.
  21. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme Learning Machine: Theory and Applications,” Neurocomputing, vol. 70, pp. 489-501, 2006.
  22. M. B. Li and M. J. Er, “Nonlinear System Identification Using Extreme Learning Machine,” in Proc. IEEE Int. Conf. on Control, Automation, Robotics and Vision, Dec. 2006, pp. 1-4.
  23. Q. Y. Zhu, G. B. Huang, and C. K. Siew, “A Fast Constructive Learning Algorithm For Single Hidden Layer Neural Networks,” in Proc. IEEE Int. Conf. on Control, Automation, Robotics and Vision, Dec. 2004, pp. 1907-1911.
  24. F. Samaria and A. Harter, “Parameterization of a stochastic model for human face identification,” in Proc. 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, Dec. 1994, pp. 138-142.
  25. AT&T face database,
  26. Online. Available: http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.tar.Z.
  27. Yale face database,
  28. Online. Available: http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
  29. V. P. Vishwakarma, S. Pandey and M. N. Gupta, “Face Recognition using Extreme Learning Machine,” in Proc. International Conference on Quality, Reliability and Infocom Technology, Delhi University, India, Dec. 2009.
  30. V. P. Vishwakarma, S. Pandey and M. N. Gupta, “A Novel Approach for Face Recognition Using DCT Coefficients Re-scaling for Illumination Normalization,” in Proc. 15th IEEE Conference on Advanced Computing & Communication (ADCOM 2007), pp.535-539, Dec. 2007.
  31. V. P. Vishwakarma, S. Pandey and M. N. Gupta, “An Illumination Invariant Accurate Face Recognition with Down Scaling of DCT Coefficients,” Journal of Computing and Information Technology, vol. 18, no. 1, pp. 53-67, Mar. 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Face recognition non-iterative learning algorithms SLFN