[1] J. Li, Y. Wu, and K. Lu, “Structured domain adaptation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 8, pp. 1700–1713, 2017.
[2] B. Gong, K. Grauman and F. Sha, “Connecting the dots with landmarks: discriminatively learning domain-invariant features for unsupervised domain adaptation”, Proceedings of the International Conference on Machine Learning, vol. 28, no. 1, pp. 222-230, 2013.
[3] 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, pp. 933–942, ACM, 2011.
[4] H. Liu and L. Yu, “Toward integrating feature selection algorithms for classification and clustering,” IEEE Transactions on knowledge and data engineering, vol. 17, no. 4, pp. 491502, 2005.
[5] J. Tahmoresnezhad and S. Hashemi, “Diret: An effective discriminative dimensionality reduction approach for multi-source transfer learning,” Scientia Iranica. Transaction D, Computer Science & Engineering, Electrical, vol. 24, no. 3, pp. 1303–1311, 2017.
[6] M. Singha, D. Deb, and S. Roy, “Hybrid feature extraction method for partial face recognition,” Int. J. Emerg. Technol. Adv. Eng. Website, vol. 4, pp. 308–312, 2014.
[7] Saenko K, Kulis B, Fritz M, Darrell T. Adapting visual category models to new domains. Computer Vision–ECCV 2010. 2010:213-26.
[8] K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European conference on computer vision, pp. 213–226, Springer, 2010.
[9] M. Long, J. Wang, G. Ding, J. Sun and P. S. Yu, “Transfer joint matching for unsupervised domain adaptation”, IEEE conference on computer vision and pattern recognition, pp. 1410-1417, 2014.
[10] Y. Aytar and A. Zisserman, “Tabula rasa: Model transfer for object category detection,” in Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 2252–2259, IEEE, 2011.
[11] G.Griffin, A. Holub and P. Perona, “Caltech-256 object category dataset”, Technical Report7694, 2007.
[12] J. J. Hull, “A database for handwritten text recognition research”, IEEE Trans. Pattern Anal. Mach. Intell, vol. 16, no. 5, pp. 550–554, 1994.
[13] T. Sim, S. Baker and M. Bsat, “The CMU pose, illumination, and expression (PIE) database”, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 53-58, 2002.
[14] M. Long, J. Wang, G. Ding, S. J. Pan, and S. Y. Philip, “Adaptation regularization: A general framework for transfer learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 5, pp. 1076–1089, 2014.
[15] J. Tahmoresnezhad and S. Hashemi, “Visual domain adaptation via transfer feature learning,” Knowledge and Information Systems, vol. 50, no. 2, pp. 585– 605, 2017.
[16] L. Luo, X. Wang, S. Hu, C. Wang, Y. Tang, and L. Chen, “Close yet distinctive domain adaptation,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 850–863, 2017.
[17] Ggbdx
[18] L. M. Bregman, “The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming,” USSR computational mathematics and mathematical physics, vol. 7, no. 3, pp. 200–217, 1967.
[19] 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, p. 929, 2010.
[20] M. Long, J. Wang, G. Ding, J. Sun and S. YuPhilip, “Transfer feature learning with joint distribution adaptation”, IEEE international conference on computer vision, pp. 2200-2207, 2013.
[21] Y. Xu, X. Fang, J. Wu, X. Li, and D. Zhang, “Discriminative transfer subspace learning via low-rank and sparse representation,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 850–863, 2016.
[22] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, “Boosting for transfer learning,” in Proceedings of the 24th international conference on Machine learning, 2007, pp. 193–200.
[23] Y. Tsuboi, H. Kashima, S. Hido, S. Bickel, and M. Sugiyama, “Direct density ratio estimation for large-scale covariate shift adaptation.” Information and Media Technologies, vol. 4, no. 2, pp. 529–546, 2009.
[24] J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, “Dataset Shift in Machine Learning”, The MIT Press, 2009.
[25] Z. Ding, M. Shao, and Y. Fu, “Deep low-rank coding for transfer learning,” in Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, 2015, pp. 3453–3459.
[26] M. Long, J. Wang, G. Ding, J. Sun, and P. Yu, “Transfer feature learning with joint distribution adaptation,” in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2200– 2207.
)