Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 36, Issue 6, 2014, Pages 1134-1148

  Li, Wen ^a   Duan, Lixin ^b   Xu, Dong ^a   Tsang, Ivor W ^c  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

augmented features; domain adaptation; Heterogeneous domain adaptation; transfer learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION;

AUGMENTED FEATURES; DOMAIN ADAPTATION; FEATURE REPRESENTATION; HETEROGENEOUS DOMAINS; HETEROGENEOUS FEATURES; MULTIPLE KERNEL LEARNING; SUPERVISED LEARNING METHODS; TRANSFER LEARNING;

DIRECTIVE ANTENNAS;

EID: 84901836467     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2013.167     Document Type: Article

References (37)

1. J. Blitzer, R. McDonald, and F. Pereira, "Domain adaptation with structural correspondence learning," in Proc. EMNLP, Sydney, NSW, Australia, 2006.
2. J. Blitzer, M. Dredze, and F. Pereira, "Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification," in Proc. 45th ACL, Prague, Czech Republic, 2007.
3. H. Daumé, III, "Frustratingly easy domain adaptation," in Proc. ACL, 2007.
4. L. Duan, D. Xu, I. W. Tsang, and J. Luo, "Visual event recognition in videos by learning from web data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 9, pp. 1667-1680, Sep. 2012.
5. L. Duan, I.W. Tsang, and D. Xu, "Domain transfer multiple kernel learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 3, pp. 465-479, Mar. 2012.
6. L. Duan, D. Xu, and S.-F. Chang, "Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach," in Proc. CVPR, Providence, RI, USA, 2012, pp. 1338-1345.
7. L. Chen, L. Duan, and D. Xu, "Event recognition in videos by learning from heterogeneous web sources," in Proc. CVPR, Portland, OR, USA, 2013, pp. 2666-2673.
8. W. Dai, Y. Chen, G.-R. Xue, Q. Yang, and Y. Yu, "Translated learning: Transfer learning across different feature spaces," in Proc. NIPS, 2009.
9. Q. Yang, Y. Chen, G.-R. Xue, W. Dai, and Y. Yu, "Heterogeneous transfer learning for image clustering via the social web," in Proc. ACL/IJCNLP, Singapore, 2009.
10. Y. Zhu et al., "Heterogeneous transfer learning for image classification," in Proc. AAAI, 2011.
11. B. Wei and C. Pal, "Cross-lingual adaptation: An experiment on sentiment classifications," in Proc. ACL, 2010.
12. P. Prettenhofer and B. Stein, "Cross-language text classification using structural correspondence learning," in Proc. ACL, 2010.
13. X. Shi, Q. Liu, W. Fan, P. S. Yu, and R. Zhu, "Transfer learning on heterogenous feature spaces via spectral transformation," in Proc. ICDM, Sydney, NSW, Australia, 2010.
14. M. Harel and S. Mannor, "Learning from multiple outlooks," in Proc. 28th ICML, Bellevue, WA, USA, 2011.
15. C. Wang and S. Mahadevan, "Heterogeneous domain adaptation using manifold alignment," in Proc. 22nd IJCAI, 2011.
16. K. Saenko, B. Kulis, M. Fritz, and T. Darrell, "Adapting visual category models to new domains," in Proc. ECCV, Heraklion, Greece, 2010.
17. B. Kulis, K. Saenko, and T. Darrell, "What you saw is not what you get: Domain adaptation using asymmetric kernel transforms," in Proc. CVPR, Providence, RI, USA, 2011.
18. L. Duan, D. Xu, and I. W. Tsang, "Learning with augmented features for heterogeneous domain adaptation," in Proc. 29th ICML, Edinburgh, Scotland, U.K., 2012, pp. 711-718.
19. M. Kloft, U. Brefeld, S. Sonnenburg, and A. Zien, "p-norm multiple kernel learning," JMLR, vol. 12, pp. 953-997, Mar. 2011.
20. M. Dudik, Z. Harchaoui, and J. Malick, "Lifted coordinate descent for learning with trace-norm regularization," in Proc. 15th AISTATS, La Palma, Spain, 2012.
21. P. V. Gehler and S. Nowozin, "Infinite kernel learning," Max Planck Institute for Biological Cybernetics, Tech. Rep. 178, 2008.
22. J. C. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel Methods. Cambridge, MA, USA: MIT Press, 1999, pp. 185-208.
23. M. Tan, L. Wang, and I. W. Tsang, "Learning sparse SVM for feature selection on very high dimensional datasets," in Proc. 27th ICML, Haifa, Israel, 2010.
24. A. Mutapcic and S. Boyd, "Cutting-set methods for robust convex optimization with pessimizing oracles," Optim. Meth. Softw., vol. 24, no. 3, pp. 381-406, Jun. 2009.
25. R. Collobert, F. Sinz, J. Weston, and L. Bottou, "Large scale transductive SVMs," JMLR, vol. 7, pp. 1687-1712, Dec. 2006. (Pubitemid 44223088)
26. X. Zhu, "Semi-supervised learning literature survey," University of Wisconsion-Madison, Tech. Rep. 1530, 2005.
27. L. Duan, D. Xu, and I. W. Tsang, "Domain adaptation from multiple sources: A domain-dependent regularization approach," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 3, pp. 504-518, Mar. 2012.
28. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, "Domain adaptation via transfer component analysis," IEEE Trans. Neural Netw. Learn. Syst., vol. 22, no. 2, pp. 199-210, Feb. 2011.
29. H. Daumé, III, A. Kumar, and A. Saha, "Co-regularization based semi-supervised domain adaptation," in Proc. NIPS, 2010.
30. Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou, "Tighter and convex maximum margin clustering," in Proc. AISTATS, Clearwater Beach, FL, USA, 2009.
31. W. Li, L. Duan, D. Xu, and I. W. Tsang, "Text-based image retrieval using progressive multi-instance learning," in Proc. ICCV, Barcelona, Spain, 2011, pp. 2049-2055.
32. W. Li, L. Duan, I. W. Tsang, and D. Xu, "Batch mode adaptive multiple instance learning for computer vision tasks," in Proc. CVPR, Providence, RI, USA, 2012, pp. 2368-2375.
33. B. Schölkopf et al., "Input space versus feature space in kernelbased methods," IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1000-1017, Sep. 1999.
34. H. Bay, T. Tuytelaars, and L. V. Gool, "SURF: Speeded up robust features," in Proc. ECCV, Graz, Austria, 2006.
35. M. Amini, N. Usunier, and C. Goutte, "Learning from multiple partially observed views - An application to multilingual text categorization," in Proc. NIPS, 2009.
36. P. Prettenhofer and B. Stein, "Cross-language text classification using structural correspondence learning," in Proc. 48th ACL, Uppsala, Sweden, 2010.
37. B. Kulis, M. Sustik, and I. Dhillon, "Low-rank kernel learning with Bregman matrix divergences," JMLR, vol. 10, pp. 341-376, Feb. 2009.