Landmarks-based kernelized subspace alignment for unsupervised domain adaptation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 56-63

  Aljundi, Rahaf ^a,b   Emonet, Rémi ^a,b   Muselet, Damien ^a,b   Sebban, Marc ^a,b  

^a UNIVERSITÉ JEAN MONNET   (France)

^b UNIVERSITÉ JEAN MONNET   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALIGNMENT; COMPUTER VISION;

CLOSED FORM; DOMAIN ADAPTATION; EXPERIMENTAL COMPARISON; LEARNING PROCESS; SUBSPACE ALIGNMENT; TARGET DOMAIN; TWO DOMAINS;

PATTERN RECOGNITION;

EID: 84959254588     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298600     Document Type: Conference Paper

References (24)

1. A. Bergamo and L. Torresani. Exploiting weakly-labeled web images to improve object classification: a domain adap-tation approach. In NIPS, volume 1, page 4, 2010
2. R. Chatpatanasiri, T. Korsrilabutr, P. Tangchanachaianan, and B. Kijsirikul. A new kernelization framework for mahalanobis distance learning algorithms. Neurocomputing, pages 1570-1579, 2010
3. 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, pages 179-188. ACM, 2009
4. M. Chen, K. Q. Weinberger, and J. Blitzer. Co-training for domain adaptation. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 2456-2464. Curran Associates, Inc., 2011
5. W.-S. Chu, F. D. la Torre, and J. F. Cohn. Selective transfer machine for personalized facial action unit detection. In Computer Vision and Pattern Recognition (CVPR), pages 3515-3522, 2013
6. H. Daumé III, A. Kumar, and A. Saha. Co-regularization based semi-supervised domain adaptation. In NIPS, volume 10, pages 478-486, 2010
7. B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars. Unsupervised visual domain adaptation using subspace alignment. In ICCV, 2013
8. B. Gong, K. Grauman, and F. Sha. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In Proceedings of The 30th International Conference on Machine Learning, pages 222-230, 2013
9. B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In Computer Vision and Pattern Recognition (CVPR), pages 2066-2073. IEEE, 2012
10. R. Gopalan, R. Li, and R. Chellappa. Domain adaptation for object recognition: An unsupervised approach. In ICCV, pages 999-1006. IEEE, 2011
11. Q. Gu, Z. Li, and J. Han. Joint feature selection and subspace learning. In Proceedings of the International Joint Confer-ences on Artificial Intelligence (IJCAI), pages 1294-1299, 2011
12. J. Huang, A. J. Smola, A. Gretton, K. M. Borgwardt, and B. Schölkopf. Correcting sample selection bias by unlabeled data. Advances in neural information processing systems, 19:601, 2007
13. B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In Computer Vision and Pattern Recognition (CVPR), pages 1785-1792. IEEE, 2011
14. M. Long, G. Ding, J. Wang, J. Sun, Y. Guo, and P. Yu. Transfer sparse coding for robust image representation. In Com-puter Vision and Pattern Recognition (CVPR), pages 407-414, June 2013
15. M. Long, J. Wang, G. Ding, J. Sun, and P. Yu. Transfer joint matching for unsupervised domain adaptation. In Computer Vision and Pattern Recognition (CVPR), pages 1410-1417, June 2014
16. A. Margolis. A literature review of domain adaptation with unlabeled data. Technical Report, 2011
17. M. Masaeli, J. G. Dy, and G. M. Fung. From transformationbased dimensionality reduction to feature selection. In J. Frnkranz and T. Joachims, editors, Proceedings of the 27th International Conference on Machine Learning (ICML), pages 751-758. Omnipress, 2010
18. R. Mehrotra, R. Agrawal, and S. A. Haider. Dictionary based sparse representation for domain adaptation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12, pages 2395-2398, New York, NY, USA, 2012. ACM
19. S. J. Pan, J. T. Kwok, and Q. Yang. Transfer learning via dimensionality reduction. In AAAI, volume 8, pages 677-682, 2008
20. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang. Domain adaptation via transfer component analysis. Neural Net-works, IEEE Transactions on, 22(2):199-210, 2011
21. S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, October 2010
22. K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, pages 213-226. Springer, 2010
23. M. Sugiyama, S. Nakajima, H. Kashima, P. Von Buenau, and M. Kawanabe. Direct importance estimation with model selection and its application to covariate shift adaptation. In NIPS, volume 7, pages 1433-1440, 2007
24. Y. Tsuboi, H. Kashima, S. Hido, S. Bickel, and M. Sugiyama. Direct density ratio estimation for large-scale covariate shift adaptation. Information and Media Technolo-gies, 4(2):529-546, 2009.