Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017, Article ID 1239164, 8 pages
https://doi.org/10.1155/2017/1239164
Research Article

Domain Adaption Based on ELM Autoencoder

School of Computer, Xi’an University of Posts & Telecommunications, Xi’an 710121, China

Correspondence should be addressed to Wan-Yu Deng; moc.621@uynawgned

Received 21 June 2016; Revised 26 November 2016; Accepted 18 May 2017; Published 19 June 2017

Academic Editor: Jason Gu

Copyright © 2017 Wan-Yu Deng 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. E. Cambria, N. Howard, Y. Xia, and T.-S. Chua, “Computational Intelligence for Big Social Data Analysis [Guest Editorial],” IEEE Computational Intelligence Magazine, vol. 11, no. 3, pp. 8-9, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 1521–1528, IEEE, Colorado Springs, RI, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. G.-B. Huang, E. Cambria, K.-A. Toh, B. Widrow, and Z. Xu, “New trends of learning in computational intelligence [Guest Editorial],” IEEE Computational Intelligence Magazine, vol. 10, no. 2, pp. 16-17, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Chen, W. Lam, I. Tsang, and T.-L. Wong, “Extracting discriminative concepts for domain adaptation in text mining,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09, pp. 179–187, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. S. J. Pan, J. T. Kwok, and Q. Yang, “Transfer learning via dimensionality reduction,” in Proceedings of the 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08, July 13, 2008–July 17, 2008, pp. 677–682, Chicago, IL, USA, 2008.
  6. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” in Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI-09, July 11, 2009–July 17, 2009, pp. 1187–1192, Pasadena, CA, USA, 2009.
  7. B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, “Unsupervised visual domain adaptation using subspace alignment,” in Proceedings of the 2013 14th IEEE International Conference on Computer Vision, ICCV 2013, pp. 2960–2967, aus, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 6, pp. 1134–1148, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Daumé III, “Frustratingly easy domain adaptation,” in Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL '07), pp. 256–263, June 2007. View at Scopus
  10. J. Blitzer, R. McDonald, and F. Pereira, “Domain adaptation with structural correspondence learning,” in Proceedings of the 11th Conference on Empirical Methods in Natural Language Proceessing, EMNLP 2006, Held in Conjunction with COLING/ACL 2006, July 22, 2006–July 23, 2006, pp. 120–128, Sydney, Australia, July 2006. View at Publisher · View at Google Scholar
  11. C. J. Leggetter and P. C. Woodland, “Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models,” Computer Speech and Language, vol. 9, no. 2, pp. 171–185, 1995. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Huang, A. J. Smola, A. Gretton, K. M. Borgwardt, and B. Scholkopf, “Correcting sample selection bias by unlabeled data,” in Peoceedings of the 20th Annual Conference on Neural Information Processing Systems, NIPS 2006, pp. 601–608, BC, Canada, 2006.
  13. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, “Analysis of representations for domain adaptation,” in Proceedings of the 20th Annual Conference on Neural Information Processing Systems, NIPS 2006, pp. 137–144, BC, Canada, 2006.
  14. S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. I.-H. Jhuo, D. Liu, D. T. Lee, and S.-F. Chang, “Robust visual domain adaptation with low-rank reconstruction,” in Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012, pp. 2168–2175, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 2066–2073, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer, New York, NY, USA, 2nd edition, 2002. View at MathSciNet
  18. L. Zwald and G. Blanchard, “On the convergence of eigenspaces in Kernel principal component analysis,” in Proceedings of the 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005, December 5, 2005–December 8, 2005, pp. 1649–1656, Vancouver, BC, Canada, 2005.
  19. G. Huang, S. Song, and K. You, “Trends in extreme learning machines: a review,” Neural Networks, vol. 61, pp. 32–48, 2015. View at Publisher · View at Google Scholar
  20. S. Wang, C. Deng, W. Lin, G. Huang, and B. Zhao, “NMF-Based Image Quality Assessment Using Extreme Learning Machine,” IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 232–243, 2017. View at Publisher · View at Google Scholar
  21. G. Huang, L. Chen, and C. 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
  22. W. Mao, S. Zhao, X. Mu, and H. Wang, “Multi-dimensional extreme learning machine,” Neurocomputing A, vol. 149, pp. 160–170, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. W.-Y. Deng, Z. Bai, G.-B. Huang, and Q.-H. Zheng, “A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics,” Neural Networks, vol. 77, pp. 14–28, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. L. L. C. Kasun, H. Zhou, G.-B. Huang, and C. M. Vong, “Representational learning with ELMs for big data,” IEEE Intelligent Systems, vol. 28, no. 6, pp. 31–34, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. C. Wang, J. Wang, and S. Gu, “Deep network based on stacked orthogonal convex incremental ELM autoencoders,” Mathematical Problems in Engineering, vol. 2016, Article ID 1649486, 17 pages, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  26. 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 MathSciNet
  27. H. Zhou, G.-B. Huang, Z. Lin, H. Wang, and Y. C. Soh, “Stacked extreme learning machines,” IEEE Transactions on Cybernetics, vol. 2, no. 2, pp. 1–13, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. W. B. Johnson and J. Lindenstrauss, “Extensions of Lipschitz mappings into a Hilbert space,” in Proceedings of the Conf. Modern Analysis and Probability, vol. 26, pp. 189–206, Amer. Math. Soc., Providence, RI. View at Publisher · View at Google Scholar · View at MathSciNet
  29. K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in Proceedings of the 11th European Conference on Computer Vision, ECCV 2010, September 5, 2010–September 11, 2010, vol. 6314, pp. 213–226, Crete, Greece. View at Publisher · View at Google Scholar
  30. R. Gopalan, R. Li, and R. Chellappa, “Domain adaptation for object recognition: An unsupervised approach,” in Proceedings of the 2011 IEEE International Conference on Computer Vision, ICCV 2011, pp. 999–1006, esp, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. P. Qin, W. Xu, and J. Guo, “An empirical convolutional neural network approach for semantic relation classification,” Neurocomputing, vol. 190, pp. 1–9, 2016. View at Publisher · View at Google Scholar · View at Scopus
  32. W. Mao, J. Xu, C. Wang, and L. Dong, “A fast and robust model selection algorithm for multi-input multi-output support vector machine,” Neurocomputing, vol. 130, pp. 10–19, 2014. View at Publisher · View at Google Scholar · View at Scopus