Domain adaptation via transfer component analysis

IEEE Transactions on Neural Networks

Volumn 22, Issue 2, 2011, Pages 199-210

  Pan, Sinno Jialin ^a   Tsang, Ivor W ^b   Kwok, James T ^c   Yang, Qiang ^c  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Dimensionality reduction; domain adaptation; Hilbert space embedding of distributions; transfer learning

Indexed keywords

COMPONENT ANALYSIS; CROSS-DOMAIN; DATA DISTRIBUTION; DATA LABELS; DATA PROPERTIES; DATA SETS; DIFFERENT DOMAINS; DIMENSIONALITY REDUCTION; DOMAIN ADAPTATION; DOMAIN DISTRIBUTION; FEATURE REPRESENTATION; LABEL INFORMATION; LARGE DATASETS; LEARNING METHODS; REAL-WORLD APPLICATION; REGRESSION MODEL; REPRODUCING KERNEL HILBERT SPACES; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; STANDARD MACHINES; TARGET DOMAIN; TEXT CLASSIFICATION; TRANSFER LEARNING;

CLASSIFICATION (OF INFORMATION); HILBERT SPACES; LEARNING ALGORITHMS; LEARNING SYSTEMS; REGRESSION ANALYSIS; TEXT PROCESSING;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; INFORMATION PROCESSING; LEARNING; METHODOLOGY; STANDARD;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; AUTOMATIC DATA PROCESSING; COMPUTER SIMULATION; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; TRANSFER (PSYCHOLOGY);

EID: 79951681949     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2091281     Document Type: Article

References (37)

1. S.-H. Fang and T.-N. Lin, "Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments," IEEE Trans. Neural Netw., vol. 19, no. 11, pp. 1973-1978, Nov. 2008.
2. Q. Yang, S. J. Pan, and V. W. Zheng, "Estimating location using Wi-Fi," IEEE Intell. Syst., vol. 23, no. 1, pp. 8-13, Jan.-Feb. 2008.
3. J. Ghosn and Y. Bengio, "Bias learning, knowledge sharing," IEEE Trans. Neural Netw., vol. 14, no. 4, pp. 748-765, Jul. 2003.
4. S. Ozawa, A. Roy, and D. Roussinov, "A multitask learning model for online pattern recognition," IEEE Trans. Neural Netw., vol. 20, no. 3, pp. 430-445, Mar. 2009.
5. S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
6. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, "Analysis of representations for domain adaptation," in Advances in Neural Information Processing Systems 19. Cambridge, MA: MIT Press, 2007, pp. 137-144.
7. L. Song, A. Smola, K. Borgwardt, and A. Gretton, "Colored maximum variance unfolding," in Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press, 2008, pp. 1385-1392.
8. H. Daumé, III, "Frustratingly easy domain adaptation," in Proc. 45th Ann. Meeting Assoc. Comput. Linguistics, Prague, Czech Republic, Jun. 2007, pp. 256-263.
9. J. Blitzer, R. McDonald, and F. Pereira, "Domain adaptation with structural correspondence learning," in Proc. Conf. Empirical Methods Natural Lang., Sydney, Australia, Jul. 2006, pp. 120-128.
10. S. J. Pan, J. T. Kwok, and Q. Yang, "Transfer learning via dimensionality reduction," in Proc. 23rd AAAI Conf. Artif. Intell., Chicago, IL, Jul. 2008, pp. 677-682.
11. R. K. 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, Nov. 2005.
12. P. von Bünau, F. C. Meinecke, F. C. Kirúly, and K. R. Müller, "Finding stationary subspaces in multivariate time series," Phys. Rev. Lett., vol. 103, no. 21, pp. 214101-1-214101-4, Sep. 2009.
13. J. Suykens, "Data visualization and dimensionality reduction using kernel maps with a reference point," IEEE Trans. Neural Netw., vol. 19, no. 9, pp. 1501-1517, Sep. 2008.
14. J. Huang, A. Smola, A. Gretton, K. M. Borgwardt, and B. Schölkopf, "Correcting sample selection bias by unlabeled data," in Advances in Neural Information Processing Systems 19. Cambridge, MA: MIT Press, 2007, pp. 601-608.
15. M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe, "Direct importance estimation with model selection and its application to covariate shift adaptation," in Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press, 2008, pp. 1433-1440.
16. S. Bickel, M. Brückner, and T. Scheffer, "Discriminative learning under covariate shift," J. Mach. Learn. Res., vol. 10, pp. 2137-2155, Sep. 2009.
17. T. Kanamori, S. Hido, and M. Sugiyama, "A least-squares approach to direct importance estimation," J. Mach. Learn. Res., vol. 10, pp. 1391- 1445, Jul. 2009.
18. M. Sugiyama, M. Kawanabe, and P. L. Chui, "Dimensionality reduction for density ratio estimation in high-dimensional spaces," Neural Netw., vol. 23, no. 1, pp. 44-59, Jan. 2010.
19. T. Suzuki and M. Sugiyama, "Estimating squared-loss mutual information for independent component analysis," in Proc. 8th Int. Conf. Independent Compon. Anal. Signal Separation, Paraty, Brazil, Mar. 2009, pp. 130-137.
20. S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, and T. Kanamori, "Inlierbased outlier detection via direct density ratio estimation," in Proc. 8th IEEE Int. Conf. Data Mining, Pisa, Italy, Dec. 2008, pp. 223-232.
21. Y. Kawahara and M. Sugiyama, "Change-point detection in time-series data by direct density-ratio estimation," in Proc. SIAM Int. Conf. Data Mining, Sparks, NV, Apr. 2009, pp. 389-400.
22. A. J. Smola, A. Gretton, L. Song, and B. Schölkopf, "A Hilbert space embedding for distributions," in Proc. 18th Int. Conf. Algorithmic Learn. Theory, Sendai, Japan, Oct. 2007, pp. 13-31.
23. A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola, "A kernel method for the two-sample problem," in Proc. Conf. Neural Inf. Process. Syst. 19. Cambridge, MA, 2007, pp. 513-520.
24. A. Gretton, O. Bousquet, A. J. Smola, and B. Schölkopf, "Measuring statistical dependence with Hilbert-Schmidt norms," in Proc. 18th Int. Conf. Algorithmic Learn. Theory, Singapore, Oct. 2005, pp. 63-77.
25. I. Steinwart, "On the influence of the kernel on the consistency of support vector machines," J. Mach. Learn. Res., vol. 2, pp. 67-93, Nov. 2001.
26. K. Q. Weinberger, F. Sha, and L. K. Saul, "Learning a kernel matrix for nonlinear dimensionality reduction," in Proc. 21st Int. Conf. Mach. Learn., Banff, AB, Canada, Jul. 2004, pp. 839-846. (Pubitemid 40290888)
27. B. Schölkopf, A. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, no. 5, pp. 1299-1319, Jul. 1998. (Pubitemid 128463674)
28. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, "An introduction to kernel-based learning algorithms," IEEE Trans. Neural Netw., vol. 12, no. 2, pp. 181-201, Mar. 2001. (Pubitemid 32371478)
29. O. Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning. Cambridge, MA: MIT Press, 2006.
30. N. Cristianini, J. Kandola, A. Elisseeff, and J. Shawe-Taylor, "On kernel-target alignment," in Advances in Neural Information Processing Systems 14. Cambridge, MA: MIT Press, 2002, pp. 367-373.
31. H. Xiong, M. Swamy, and M. Ahmad, "Optimizing the kernel in the empirical feature space," IEEE Trans. Neural Netw., vol. 16, no. 2, pp. 460-474, Mar. 2005. (Pubitemid 40390828)
32. 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. (Pubitemid 44708005)
33. Y. Nesterov and A. Nemirovskii, Interior-Point Polynomial Algorithms in Convex Programming. Philadelphia, PA: SIAM, 1994.
34. D. C. Sorensen, "Implicitly restarted Arnoldi/Lanczos methods for large scale eigenvalue calculations," Dept. Comput. Appl. Math., Rice Univ., Houston, TX, Tech. Rep. TR-96-40, 1996.
35. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, "Domain adaptation via transfer component analysis," in Proc. 21st Int. Joint Conf. Artif. Intell., Pasadena, CA, Jul. 2009, pp. 1187-1192.
36. K. Lang, "Newsweeder: Learning to filter netnews," in Proc. 12th Int. Conf. Mach. Learn., San Mateo, CA, 1995, pp. 331-339.
37. W. Dai, G. Xue, Q. Yang, and Y. Yu, "Co-clustering based classification for out-of-domain documents," in Proc. 13th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, San Jose, CA, Aug. 2007, pp. 210-219. (Pubitemid 350229208)