Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 1649486, 17 pages
http://dx.doi.org/10.1155/2016/1649486
Research Article

Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Received 1 March 2016; Revised 7 May 2016; Accepted 31 May 2016

Academic Editor: Lotfi Senhadji

Copyright © 2016 Chao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. G.-B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” International Journal of Machine Learning and Cybernetics, vol. 2, no. 2, pp. 107–122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. X.-Z. Wang, Q.-Y. Shao, Q. Miao, and J.-H. Zhai, “Architecture selection for networks trained with extreme learning machine using localized generalization error model,” Neurocomputing, vol. 102, pp. 3–9, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. A. M. Fu, C. R. Dong, and L. S. Wang, “An experimental study on stability and generalization of extreme learning machines,” International Journal of Machine Learning and Cybernetics, vol. 6, no. 1, pp. 129–135, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. X.-Z. Wang, R. A. R. Ashfaq, and A.-M. Fu, “Fuzziness based sample categorization for classifier performance improvement,” Journal of Intelligent and Fuzzy Systems, vol. 29, no. 3, pp. 1185–1196, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Wu, S. T. Wang, and F.-L. Chung, “Positive and negative fuzzy rule system, extreme learning machine and image classification,” International Journal of Machine Learning and Cybernetics, vol. 2, no. 4, pp. 261–271, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Lu, X. Wang, G. Zhang, and X. Zhou, “Effective algorithms of the Moore-Penrose inverse matrices for extreme learning machine,” Intelligent Data Analysis, vol. 19, no. 4, pp. 743–760, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. G.-B. Huang, L. Chen, and C.-K. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Zhang, S. Ding, N. Zhang, and Z. Shi, “Incremental extreme learning machine based on deep feature embedded,” International Journal of Machine Learning and Cybernetics, vol. 7, no. 1, pp. 111–120, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Ye and Y. Qin, “QR factorization based Incremental Extreme Learning Machine with growth of hidden nodes,” Pattern Recognition Letters, vol. 65, pp. 177–183, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. J.-L. Ding, F. Wang, H. Sun, and L. Shang, “Improved incremental regularized extreme learning machine algorithm and its application in two-motor decoupling control,” Neurocomputing, vol. 149, pp. 215–223, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Xu, M. Yao, Z. Wu, and W. Dai, “Incremental regularized extreme learning machine and it's enhancement,” Neurocomputing, vol. 174, pp. 134–142, 2016. View at Publisher · View at Google Scholar
  13. G.-B. Huang, M.-B. Li, L. Chen, and C.-K. Siew, “Incremental extreme learning machine with fully complex hidden nodes,” Neurocomputing, vol. 71, no. 4–6, pp. 576–583, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Li, “Orthogonal incremental extreme learning machine for regression and multiclass classification,” Neural Computing & Applications, vol. 27, no. 1, pp. 111–120, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. G.-B. Huang and L. Chen, “Convex incremental extreme learning machine,” Neurocomputing, vol. 70, no. 16–18, pp. 3056–3062, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Ding, N. Zhang, X. Xu, L. Guo, and J. Zhang, “Deep extreme learning machine and its application in EEG classification,” Mathematical Problems in Engineering, vol. 2015, Article ID 129021, 11 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. O. Vinyals, Y. Jia, L. Deng, and T. Darrell, “Learning with recursive perceptual representations,” in Advances in Neural Information Processing Systems, pp. 2825–2833, 2012. View at Google Scholar
  19. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, vol. 2, no. 25, MIT Press, 2012. View at Google Scholar
  20. R. Socher, J. Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning, “Semi-supervised recursive autoencoders for predicting sentiment distributions,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '11), pp. 151–161, Association for Computational Linguistics, July 2011. View at Scopus
  21. Y. Bengio and O. Delalleau, “On the expressive power of deep architectures,” in Algorithmic Learning Theory, J. Kivinen, C. Szepesvári, E. Ukkonen, and T. Zeugmann, Eds., vol. 6925 of Lecture Notes in Computer Science, pp. 18–36, Springer, New York, NY, USA, 2011. View at Publisher · View at Google Scholar
  22. Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–27, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  23. Y. Bengio and Y. Lecun, “Scaling learning algorithms towards AI,” Large-Scale Kernel Machines, vol. 2007, no. 34, pp. 1–41, 2007. View at Google Scholar
  24. T. S. Shores, Applied Linear Algebra and Matrix Analysis, Springer, Berlin, Germany, 2007.
  25. G. Taguchi and R. Jugulum, The Mahalanobis Taguchi Strategy: A Pattern Technology System, John Wiley & Sons, Hoboken, NJ, USA, 2002. View at Publisher · View at Google Scholar
  26. Y. M. Yang, Y. N. Wang, and X. F. Yuan, “Parallel chaos search based incremental extreme learning machine,” Neural Processing Letters, vol. 37, no. 3, pp. 277–301, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. Q. Yu, Y. Miche, E. Séverin, and A. Lendasse, “Bankruptcy prediction using Extreme Learning Machine and financial expertise,” Neurocomputing, vol. 128, pp. 296–302, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. K. I. Wong, M. V. Chi, P. K. Wong et al., “Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction,” Neurocomputing, vol. 2015, no. 149, pp. 397–404, 2015. View at Google Scholar
  29. L. L. C. Kasun, H. Zhou, G. B. Huang, and C. M. Vong, “Representational learning with extreme learning machine,” IEEE Intelligent Systems, vol. 6, no. 28, pp. 31–34, 2013. View at Google Scholar
  30. W. Johnson and J. Lindenstrauss, “Extensions of Lipschitz maps into a Hilbert space,” Modern Analysis and Probability, vol. 189, no. 26, pp. 189–206, 1984. View at Google Scholar
  31. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. G. E. Hinton, “A practical guide to training restricted Boltzmann machines,” Momentum, vol. 1, no. 9, pp. 599–619, 2010. View at Google Scholar
  33. R. Salakhutdinov and H. Larochelle, “Efficient learning of deep Boltzmann machines,” in Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS '10), vol. 9 of JMLR: Workshop and Conference Proceedings, pp. 693–700, 2010.
  34. H. Zhou, G. B. Huang, Z. Lin et al., “Stacked extreme learning machines,” IEEE Transactions on Cybernetics, vol. 2, no. 2, pp. 1–13, 2014. View at Google Scholar
  35. M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intelligent Systems, vol. 13, no. 4, pp. 18–28, 1998. View at Publisher · View at Google Scholar
  36. H. Yu, P. D. Reiner, T. Xie, T. Bartczak, and B. M. Wilamowski, “An incremental design of radial basis function networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 10, pp. 1793–1803, 2014. View at Publisher · View at Google Scholar · View at Scopus