Learning with augmented features for heterogeneous domain adaptation

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 711-718

  Duan, Lixin ^a   Xu, Dong ^a   Tsang, Ivor W ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATING OPTIMIZATIONS; BENCHMARK DATASETS; FEATURE MAPPING; FEATURE REPRESENTATION; HETEROGENEOUS DOMAINS; HETEROGENEOUS FEATURES; HIGH DIMENSIONS; KERNELIZATION; LEARNING METHODS; LOSS FUNCTIONS; NONTRIVIAL OPTIMIZATION; OBJECTIVE FUNCTIONS; PROJECTION MATRIX; TARGET DOMAIN; TWO DOMAINS;

ALGORITHMS; DIRECTIVE ANTENNAS;

LEARNING SYSTEMS;

EID: 84867113087     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Amini, M., Usunier, N., and Goutte, C. Learning from multiple partially observed views - an application to multilingual text categorization. In NIPS, 2009.
2. Bay, H., Tuytelaars, T., and Gool, L. Van. Surf: Speeded up robust features. In ECCV, 2006.
3. Blitzer, J., McDonald, R., and Pereira, F. Domain adaptation with structural correspondence learning. In EMNLP, 2006.
4. Blitzer, J., Dredze, M., and Pereira, F. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, 2007.
5. Boyd, Stephen and Vandenberghe, Lieven. Convex Optimization. Cambridge University Press, 2004.
6. Dai, W., Chen, Y., Xue, G.-R., Yang, Q., and Yu, Y. Translated learning: Transfer learning across different feature spaces. In NIPS, 2009.
7. Daumé III, H. Frustratingly easy domain adaptation. In ACL, 2007.
8. Duan, L., Xu, D., Tsang, I. W., and Luo, J. Visual event recognition in videos by learning from web data. In CVPR, 2010.
9. Duan, L., Tsang, I. W., and Xu, D. Domain transfer multiple kernel learning. T-PAMI, 34(3):465-479, March 2012a.
10. Duan, L., Xu, D., and Tsang, I. W. Domain adaptation from multiple sources: A domain-dependent regularization approach. T-NNLS, 23(3):504-518, March 2012b.
11. Harel, M. and Mannor, S. Learning from multiple outlooks. In ICML, 2011.
12. Kulis, B., Saenko, K., and Darrell, T. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011.
13. Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., and Chen, Z. Cross-domain sentiment classification via spectral feature alignment. In WWW, 2010.
14. Prettenhofer, P. and Stein, B. Cross-language text classification using structural correspondence learning. In ACL, 2010.
15. Saenko, K., Kulis, B., Fritz, M., and Darrell, T. Adapting visual category models to new domains. In ECCV, 2010.
16. Shawe-Taylor, J. and Cristianini, N. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
17. Shi, X., Liu, Q., Fan, W., Yu, P. S., and Zhu, R. Transfer learning on heterogenous feature spaces via spectral transformation. In ICDM, 2010.
18. Vandenberghe, L. and Boyd, S. Semidefinite programming. SIAM Review, 38(1):49-95, March 1996.
19. Wang, C. and Mahadevan, S. Heterogeneous domain adaptation using manifold alignment. In IJCAI, 2011.
20. Wei, B. and Pal, C. Cross-lingual adaptation: An experiment on sentiment classifications. In ACL, 2010.
21. Wu, P. and Dietterich, T. G. Improving svm accuracy by training on auxiliary data sources. In ICML, 2004.
22. Yang, Q., Chen, Y., Xue, C-R., Dai, W., and Yu, Y. Heterogeneous transfer learning for image clustering via the social web. In ACL/IJCNLP, 2009.
23. Zhu, Y., Chen, Y., Lu, Z., Pan, S. J., Xue, G.-R., Yu, Y., and Yang, Q. Heterogeneous transfer learning for image classification. In AAAI, 2011.