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Mathematical Problems in Engineering
Volume 2015, Article ID 329753, 8 pages
http://dx.doi.org/10.1155/2015/329753
Research Article

Multiview Sample Classification Algorithm Based on L1-Graph Domain Adaptation Learning

School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Received 14 November 2014; Revised 4 February 2015; Accepted 7 February 2015

Academic Editor: Gerhard-Wilhelm Weber

Copyright © 2015 Huibin Lu 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. 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
  2. T. Li, V. Sindhwani, C. Ding, and Y. Zhang, “Knowledge transformation for cross-domain sentiment classification,” in Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '09), pp. 716–717, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Luo, D. Tao, B. Geng, C. Xu, and S. J. Maybank, “Manifold regularized multitask learning for semi-supervised multilabel image classification,” IEEE Transactions on Image Processing, vol. 22, no. 2, pp. 523–536, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. Y. Luo, T. Liu, D. Tao, and C. Xu, “Decomposition-based transfer distance metric learning for image classification,” IEEE Transactions on Image Processing, vol. 23, no. 9, pp. 3789–3801, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  5. S. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 7, pp. 929–942, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Dai, G.-R. Xue, Q. Yang, and Y. Yu, “Co-clustering based classification for out-of-domain documents,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 210–219, 2007.
  7. F. Zhuang, P. Luo, H. Xiong, Q. He, Y. Xiong, and Z. Shi, “Exploiting associations between word clusters and document classes for cross-domain text categorization,” Statistical Analysis and Data Mining, vol. 4, no. 1, pp. 100–114, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. H. Wang, H. Huang, F. Nie, and C. Ding, “Cross-language web page classification via dual knowledge transfer using nonnegative matrix tri-factorization,” in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '11), pp. 933–942, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Long, J. Wang, G. Ding, W. Cheng, X. Zhang, and W. Wang, “Dual transfer learning,” in Proceedings of the 12th SIAM International Conference on Data Mining, pp. 540–551, 2012.
  10. F. Zhuang, P. Luo, C. Du, Q. He, and Z. Shi, “Triplex transfer learning: exploiting both shared and distinct concepts for text classification,” in Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM '13), pp. 425–434, it, February 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. D. Cai, X. He, X. Wang, H. Bao, and J. Han, “Locality preserving nonnegative matrix factorization,” in Proceedings of the 21st International Joint Conference on Artificial Intelligence, pp. 1010–1015, July 2009. View at Scopus
  12. X. Ling, W. Dai, G.-R. Xue, Q. Yang, and Y. Yu, “Spectral domain-transfer learning,” in Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining, pp. 488–496, 2008.
  13. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE Transactions on Neural Networks, vol. 22, no. 2, pp. 199–210, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Wang and S. Mahadevan, “Heterogeneous domain adaptation using manifold alignment,” in Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 1541–1546, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Long, J. Wang, G. Ding, D. Shen, and Q. Yang, “Transfer learning with graph regularization,” in Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, pp. 1033–1039, July 2012. View at Scopus
  16. B. Cheng, J. Yang, B. Yan, and Y. Fu, “Learning with l1-graph for image analysis,” IEEE Transactions on Image Processing, vol. 19, no. 4, pp. 858–866, 2010. View at Publisher · View at Google Scholar
  17. Y. Fang, R. Wang, B. Dai, and X. Wu, “Graph-based learning via auto-grouped sparse regularization and kernelized extension,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 1, pp. 142–154, 2015. View at Publisher · View at Google Scholar
  18. J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, “Information-theoretic metric learning,” in Proceedings of the 24th International Conference on Machine Learning (ICML '07), pp. 209–216, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Kate, K. Brian, F. Mario, and D. Trevor, “Adapting visual category models to new domains,” in Proceedings of the 11th European Conference on Computer Vision, Part IV, Heraklion, Greece, September 2010, vol. 6314, pp. 213–226, Springer, 2010. View at Google Scholar