Heterogeneous domain adaptation and classification by exploiting the correlation subspace

IEEE Transactions on Image Processing

Volumn 23, Issue 5, 2014, Pages 2009-2018

  Yeh, Yi Ren ^a   Huang, Chun Hao ^b   Wang, Yu Chiang Frank ^c  

^a CHINESE CULTURE UNIVERSITY   (Taiwan)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c RESEARCH CENTER FOR INFORMATION TECHNOLOGY INNOVATION   (Taiwan)

Author keywords

Canonical correlation analysis; domain adaptation; reduced kernels; support vector machine.

Indexed keywords

IMAGE CLASSIFICATION; SUPPORT VECTOR MACHINES; TEXT PROCESSING;

CANONICAL CORRELATION ANALYSIS; CLASSIFICATION APPROACH; DOMAIN ADAPTATION; HANDWRITTEN DIGIT RECOGNITION; HETEROGENEOUS DOMAINS; NON-LINEAR CORRELATIONS; PATTERN RECOGNITION PROBLEMS; REDUCED KERNELS;

CHARACTER RECOGNITION;

EID: 84898037835     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2310992     Document Type: Article

References (28)

1. S. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
2. J. Blitzer, D. Foster, and S. Kakade, "Domain adaptation with coupled subspaces," in Proc. Int. Conf. Artif. Intell. Statist., 2011, pp. 173-181.
3. K. Saenko, B. Kulis, M. Fritz, and T. Darrell, "Adapting visual category models to new domains," in Proc. Eur. Conf. Comput. Vis., 2010, pp. 213-226.
4. R. Gopalan, R. Li, and R. Chellappa, "Domain adaptation for object recognition: An unsupervised approach," in Proc. IEEE ICCV, Nov. 2011, pp. 999-1006.
5. B. Gong, Y. Shi, F. Sha, and K. Grauman, "Geodesic flow kernel for unsupervised domain adaptation," in Proc. IEEE Int. Conf. CVPR, Jun. 2012, pp. 2066-2073.
6. A. Farhadi and M. Tabrizi, "Learning to recognize activities from the wrong view point," in Proc. 10th Eur. Conf. Comput. Vis., 2008, pp. 154-166.
7. J. Liu, M. Shah, B. Kuipers, and S. Savarese, "Cross-view action recognition via view knowledge transfer," in Proc. IEEE Int. Conf. CVPR, Jun. 2011, pp. 3209-3216.
8. R. Li and T. Zickler, "Discriminative virtual views for cross-view action recognition," in Proc. IEEE Int. Conf. CVPR, Jun. 2012, pp. 2855-2862.
9. T. van Kasteren, G. Englebienne, and B. Kröse, "Transferring knowledge of activity recognition across sensor networks," in Proc. 8th Int. Conf. Pervas. Comput., 2010, pp. 283-300.
10. S. Pan, J. Kwok, and Q. Yang, "Transfer learning via dimensionality reduction," in Proc. 23rd AAAI Nat. Conf. Artif. Intell., 2008, pp. 677-682.
11. S. Pan, I. Tsang, J. Kwok, and Q. Yang, "Domain adaptation via transfer component analysis," in Proc. 21st Int. Joint Conf. Artif. Intell., 2009, pp. 1187-1192.
12. H. Hotelling, "Relations between two sets of variates," Biometrika, vol. 28, nos. 3-4, pp. 321-377, 1936.
13. N. Rasiwasia et al., "A new approach to cross-modal multimedia retrieval," in Proc. ACM Int. Conf. Multimedia, 2010, pp. 251-260.
14. Y.-J. Lee and S.-Y. Huang, "Reduced support vector machines: A statistical theory," IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 1-13, Jan. 2007. (Pubitemid 46062912)
15. J. Huang, A. Smola, A. Gretton, K. Borgwardt, and B. Scholkopf, "Correcting sample selection bias by unlabeled data," in Proc. Adv. NIPS, 2007, pp. 601-608.
16. 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 Proc. Adv. NIPS, 2007, pp. 1433-1440.
17. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie, "Behavior recognition via sparse spatiooral features," in Proc. 2nd Joint IEEE Int. Workshop VS-PETS, Oct. 2005, pp. 65-72. (Pubitemid 46178025)
18. I. Laptev, "On space-time interest points," Int. J. Comput. Vis., vol. 64, no. 2, pp. 107-123, 2005. (Pubitemid 41314645)
19. B. Kulis, K. Saenko, and T. Darrell, "What you saw is not what you get: Domain adaptation using asymmetric kernel transforms," in Proc. IEEE Conf. CVPR, Jun. 2011, pp. 1785-1792.
20. L. Duan, D. Xu, and I. W. Tsang, "Learning with augmented features for heterogeneous domain adaptation," in Proc. Int. Conf. Mach. Learn., 2012, pp. 711-718.
21. D. Hardoon, S. Szedmak, and J. Shawe-Taylor, "Canonical correlation analysis: An overview with application to learning methods," Neural Comput., vol. 16, no. 12, pp. 2639-2364, 2004.
22. C. Williams and M. Seeger, "Using the Nyström method to speed up kernel machines," in Proc. Adv. NIPS, 2001, pp. 682-688.
23. P. Drineas and M. W. Mahoney, "On the Nyström method for approximating a gram matrix for improved kernel-based learning," J. Mach. Learn. Res., vol. 6, pp. 2153-2175, Dec. 2005. (Pubitemid 41832630)
24. Y. Lee and O. Mangasarian, "SSVM: A smooth support vector machine for classification," Comput. Optim. Appl., vol. 20, no. 1, pp. 5-22, 2001. (Pubitemid 32797935)
25. D. Weinland, R. Ronfard, and E. Boyer, "Free viewpoint action recognition using motion history volumes," Comput. Vis. Image Understand., vol. 104, nos. 2-3, pp. 249-257, 2006. (Pubitemid 44573869)
26. A. Frank and A. Asuncion. (2010) UCI Machine Learning Repository [Online]. Available: http://archive.ics.uci.edu/ml
27. D. Blei, A. Ng, and M. Jordan, "Latent dirichlet allocation," J. Mach. Learn. Res., vol. 3, pp. 993-1022, Mar. 2003.
28. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.