Semi-supervised domain adaptation on manifolds

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 12, 2014, Pages 2240-2249

  Cheng, Li ^a   Pan, Sinno Jialin ^b  

^a BIOINFORMATICS INSTITUTE   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Domain adaptation; semi supervised learning; transfer learning.

Indexed keywords

ITERATIVE METHODS; LEARNING ALGORITHMS; MATHEMATICAL TRANSFORMATIONS; METADATA; SUPERVISED LEARNING;

COMPETITIVE PERFORMANCE; DOMAIN ADAPTATION; ITERATIVE COORDINATE DESCENTS; OPTIMIZATION FORMULATIONS; SEMI- SUPERVISED LEARNING; SPECIAL ORTHOGONAL GROUP; TRANSFER LEARNING; TRANSFORMATION FUNCTIONS;

LINEAR TRANSFORMATIONS;

EID: 84913588904     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2014.2308325     Document Type: Article

References (31)

1. H. Daumé, "Frustratingly easy domain adaptation," in Proc. ACL, 2007, pp. 256-263
2. J. Blitzer, M. Dredze, and F. Pereira, "Biographies, Bollywood, boomboxes and blenders: Domain adaptation for sentiment classification," in Proc. ACL, 2007, pp. 432-439
3. S. Pan, J. Kwok, and Q. Yang, "Transfer learning via dimensionality reduction," in Proc. AAAI 23rd Nat. Conf. Artif. Intell., 2008, pp. 677-682
4. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, "Analysis of representations for domain adaptation," in Proc. NIPS, 2007, pp. 137-144
5. S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman, "A theory of learning from different domains," Mach. Learn., vol. 79, nos. 1-2, pp. 151-175, 2010
6. S. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010
7. J. Blitzer, R. McDonald, and F. Pereira, "Domain adaptation with structural correspondence learning," in Proc. EMNLP, 2006, pp. 120-128
8. R. Ando and T. Zhang, "A framework for learning predictive structures from multiple tasks and unlabeled data," J. Mach. Learn. Res., vol. 6, pp. 1817-1853, Dec. 2005
9. H. Daumé, A. Kumar, and A. Saha, "Co-regularization based semisupervised domain adaptation," in Proc. NIPS, 2010, pp. 478-486
10. J. Donahue, J. Hoffman, E. Rodner, K. Saenko, and T. Darrell, "Semisupervised domain adaptation with instance constraints," in Proc. IEEE CVPR, Jun. 2013, pp. 668-675
11. S. Pan, I. Tsang, J. Kwok, and Q. Yang, "Domain adaptation via transfer component analysis," IEEE Trans. Neural Netw., vol. 22, no. 2, pp. 199-210, Feb. 2011
12. G. Skolidis and G. Sanguinetti, "Semisupervised multitask learning with Gaussian processes," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 12, pp. 2101-2112, Dec. 2013
13. O. Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning. Cambridge, MA, USA: MIT Press, 2006
14. K. Saenko, B. Kulis, M. Fritz, and T. Darrell, "Adapting visual category models to new domains," in Proc. 11th ECCV, 2010, pp. 213-226
15. 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
16. L. Duan, I. Tsang, D. Xu, and T. Chua, "Domain adaptation from multiple sources via auxiliary classifiers," in Proc. 26th Annu. ICML, 2009, pp. 289-296
17. A. Edelman, T. Arias, and S. Smith, "The geometry of algorithms with orthogonality constraints," SIAM J. Matrix Anal. Appl, vol. 20, no. 2, pp. 303-353, 1998
18. J. Lee, Introduction to Smooth Manifolds. New York, NY, USA: Springer-Verlag, 2003
19. P. Absil, R. Mahony, and R. Sepulchre, Optimization Algorithms on Matrix Manifolds. Princeton, NJ, USA: Princeton Univ. Press, 2008
20. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples," J. Mach. Learn. Res., vol. 7, pp. 2399-2434, Nov. 2006
21. D. Bertsekas, Convex Analysis and Optimization. Belmont, MA, USA: Athena Scientific, 2003
22. K. Tsuda, G. Ratsch, and M. Warmuth, "Matrix exponentiated gradient updates for on-line learning and Bregman projections," J. Mach. Learn. Res., vol. 6, pp. 995-1018, Dec. 2005
23. C. Kelley, iterative methods for optimization. Philadelphia, PA, USA: SIAM, 1999
24. Y. Yang, "Globally convergent optimization algorithms on Riemannian manifolds: Uniform framework for unconstrained and constrained optimization," J. Optim. Theory Appl., vol. 132, no. 2, pp. 245-265, 2007
25. N. Higham, "The scaling and squaring method for the matrix exponential revisited," SIAM J. Matrix Anal. Appl., vol. 26, no. 4, pp. 1179-1193, 2005
26. C. Moler and C. Loan, "Nineteen dubious ways to compute the exponential of a matrix," SIAM Rev., vol. 20, no. 4, pp. 801-836, 1978
27. Q. Sun, R. Chattopadhyay, S. Panchanathan, and J. Ye, "A two-stage weighting framework for multi-source domain adaptation," in Proc. NIPS, 2011, pp. 505-513
28. J. Gao, W. Fan, J. Jiang, and J. Han, "Knowledge transfer via multiple model local structure mapping," in Proc. 14th KDD, 2008, pp. 283-291
29. E. Zhong et al., "Cross domain distribution adaptation via kernel mapping," in Proc. 15th KDD, 2009, pp. 1027-1036
30. J. Huang, A. Gretton, B. Scholkopf, A. Smola, and K. Borgwardt, "Correcting sample selection bias by unlabeled data," in Proc. NIPS, 2007, pp. 601-608
31. L. Xu, M. White, and D. Schuurmans, "Optimal reverse prediction: A unified perspective on supervised, unsupervised and semi-supervised learning," in Proc. ICML, 2009, pp. 143-151