Discovering low-rank shared concept space for adapting text mining models

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 35, Issue 6, 2013, Pages 1284-1297

  Chen, Bo ^a   Lam, Wai ^a   Tsang, Ivor W ^b   Wong, Tak Lam ^c  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c THE EDUCATION UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Domain adaptation; Low rank concept extraction; Text mining

Indexed keywords

ADAPTATION MODELS; CONCEPT EXTRACTION; DOCUMENT CLASSIFICATION; DOMAIN ADAPTATION; FEATURE SPACE; REPRODUCING KERNEL HILBERT SPACES; TEXT MINING; UNLABELED DATA;

GEOMETRY; INFORMATION RETRIEVAL SYSTEMS; TEXT PROCESSING;

DATA MINING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; DATA MINING; FACTUAL DATABASE; HUMAN; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA MINING; DATABASES, FACTUAL; HUMANS;

EID: 84884990701     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2012.243     Document Type: Article

References (44)

1. J. Huang, A. Smola, A. Gretton, K.M. Borgwardt, and B. Schölkopf, "Correcting Sample Selection Bias by Unlabeled Data," Advances in Neural Information Processing Systems 19, pp. 601-608, 2007.
2. A. Storkey and M. Sugiyama, "Mixture Regression for Covariate Shift," Advances in Neural Information Processing Systems 19, pp. 1337-1344, 2007.
3. M. Sugiyama, S. Nakajima, H. Kashima, P. von Bunau, and M. Kawanabe, "Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation," Advances in Neural Information Processing Systems 20, 2008.
4. A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Borgwardt, "Covariate Shift by Kernel Mean Matching," Data Set Shift in Machine Learning, J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, eds., pp. 131-160, MIT Press, 2009.
5. S. Bickel, M. Brückner, and T. Scheffer, "Discriminative Learning under Covariate Shift," J. Machine Learning Research, vol. 10, pp. 2137-2155, 2009.
6. J. Blitzer, R. McDonald, and F. Pereira, "Domain Adaptation with Structural Correspondence Learning," Proc. Conf. Empirical Methods in Natural Language Processing, pp. 120-128, 2006.
7. S.J. Pan, J.T. Kwok, and Q. Yang, "Transfer Learning via Dimensionality Reduction," Proc. 23rd AAAI Conf. Artificial Intelligence, pp. 677-682, 2008.
8. S.J. Pan, I.W. Tsang, J.T. Kwok, and Q. Yang, "Domain Adaptation via Transfer Component Analysis," IEEE Trans. Neural Networks, vol. 22, no. 2, pp. 199-210, Feb. 2011.
9. B. Chen, W. Lam, I. Tsang, and T.-L. Wong, "Extracting Discriminative Concepts for Domain Adaptation in Text Mining," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 179-188, 2009.
10. A. Arnold, R. Nallapati, and W.W. Cohen, "A Comparative Study of Methods for Transductive Transfer Learning," Proc. Seventh IEEE Int'l Conf. Data Mining Workshops, pp. 77-82, 2007.
11. R.K. Ando and T. Zhang, "A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data," J. Machine Learning Research, vol. 6, pp. 1817-1853, 2005.
12. S. Ji, L. Tang, S. Yu, and J. Ye, "Extracting Shared Subspace for Multi-Label Classification," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 381-389, 2008.
13. B. Schölkopf, A.J. Smola, and K.-R. Müller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, 1998. (Pubitemid 128463674)
14. A. Gretton, K. Borgwardt, M. Rasch, B. Schölkolpf, and A. Smola, "A Kernel Method for the Two-Sample Problem," Advances in Neural Information Processing Systems19, pp. 513-520, 2007.
15. K.M. Borgwardt, A. Gretton, M.J. Rasch, H.-P. Kriegel, B. Schölkopf, and A.J. Smola, "Integrating Structured Biological Data by Kernel Maximum Mean Discrepancy," Bioinformatics, vol. 22, pp. 49-57, 2006.
16. K.M. Borgwardt, A. Gretton, M.J. Rasch, H.-P. Kriegel, B. Schölkopf, and A.J. Smola, "Integrating Structured Biological Data by Kernel Maximum Mean Discrepancy," Proc. 14th Ann. Int'l Conf. Intelligence Systems for Molecular Biology, pp. 49-57, 2006.
17. T. Zhang, "Solving Large Scale Linear Prediction Problems Using Stochastic Gradient Descent Algorithms," Proc. 21st Int'l Conf. Machine Learning, 2004.
18. T. Joachims, Learning to Classify Text Using Support Vector Machines - Methods, Theory, and Algorithms. Kluwer/Springer, 2002.
19. T. Joachims, "Transductive Inference for Text Classification Using Support Vector Machines," Proc. 16th Int'l Conf. Machine Learning, pp. 200-209, 1999.
20. X. Ling, W. Dai, G.-R. Xue, Q. Yang, and Y. Yu, "Spectral Domain-Transfer Learning," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 488-496, 2008.
21. J. Jiang and C. Zhai, "A Two-Stage Approach to Domain Adaptation for Statistical Classifiers," Proc. 16th ACM Conf. Information and Knowledge Management, pp. 401-410, 2007.
22. J. Demšar, "Statistical Comparisons of Classifiers over Multiple Data Sets," J. Machine Learning Research, vol. 7, pp. 1-30, 2006. (Pubitemid 43022939)
23. T.-L. Wong and W. Lam, "Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 4, pp. 523-536, Apr. 2010.
24. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, "Analysis of Representations for Domain Adaptation," Advances in Neural Information Processing Systems 19, pp. 137-144, 2006.
25. J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman, "Learning Bounds for Domain Adaptation," Advances in Neural Information Processing Systems 20, 2007.
26. J. Blitzer, M. Dredze, and F. Pereira, "Biographies, Bollywood, Boom-Boxes and Blenders: Domain Adaptation for Sentiment Classification," Proc. 45th Ann. Meeting Assoc. for Computational Linguistics, 2007.
27. R. Raina, A. Battle, H. Lee, B. Packer, and A.Y. Ng, "Self-Taught Learning: Transfer Learning from Unlabeled Data," Proc. 24th Int'l Conf. Machine Learning, pp. 759-766, 2007.
28. W. Dai, G.-R. Xue, Q. Yang, and Y. Yu, "Co-Clustering Based Classification for Out-of-Domain Documents," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 210-219, 2007.
29. G.-R. Xue, W. Dai, Q. Yang, and Y. Yu, "Topic-Bridged PLSA for Cross-Domain Text Classification," Proc. 31st Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 627-634, 2008.
30. T. Hofmann, "Unsupervised Learning by Probabilistic Latent Semantic Analysis," Machine Learning, vol. 42, nos. 1/2, pp. 177-196, 2001. (Pubitemid 32872403)
31. J. Yang, R. Yan, and A.G. Hauptmann, "Cross-Domain Video Concept Detection Using Adaptive Svms," Proc. 15th ACM Int'l Conf. Multimedia, 2007.
32. B. Zadrozny, "Learning and Evaluating Classifiers under Sample Selection Bias," Proc. 21st Int'l Conf. Machine Learning, 2004.
33. W. Fan, I. Davidson, B. Zadrozny, and P.S. Yu, "An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias," Proc. IEEE Fifth Int'l Conf. Data Mining, pp. 605-608, 2005.
34. J. Jiang and C. Zhai, "Instance Weighting for Domain Adaptation in NLP," Proc. 45th Ann. Meeting Assoc. for Computational Linguistics, pp. 264-271, 2007.
35. S. Bickel, C. Sawade, and T. Scheffer, "Transfer Learning by Distribution Matching for Targeted Advertising," Advances in Neural Information Processing Systems 20, pp. 145-152, 2008.
36. W. Dai, G.-R. Xue, Q. Yang, and Y. Yu, "Transferring Naive Bayes Classifiers for Text Classification," Proc. 22nd AAAI Conf. Artificial Intelligence, pp. 540-545, 2007.
37. E. Zhong, W. Fan, J. Peng, K. Zhang, J. Ren, D.S. Turaga, and O. Verscheure, "Cross Domain Distribution Adaptation via Kernel Mapping," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 1027-1036, 2009.
38. H. Daumé III, "Frustratingly Easy Domain Adaptation," Proc. 45th Ann. Meeting Assoc. for Computational Linguistics, pp. 256-263, June 2007.
39. K.Q. Weinberger, A. Dasgupta, J. Langford, A.J. Smola, and J. Attenberg, "Feature Hashing for Large Scale Multitask Learning," Proc. 26th Int'l Conf. Machine Learning, pp. 1113-1120, 2009.
40. J.R. Finkel and C.D. Manning, "Hierarchical Bayesian Domain Adaptation," Proc. Human Language Technologies: Ann. Conf. North Am. Chapter Assoc. for Computational Linguistics, pp. 602-610, 2009.
41. Y. Xue, X. Liao, L. Carin, and B. Krishnapuram, "Multi-Task Learning for Classification with Dirichlet Process Priors," J. Machine Learning Research, vol. 8, pp. 35-63, 2007. (Pubitemid 46155123)
42. T. Evgeniou and M. Pontil, "Regularized Multi-Task Learning," Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 109-117, 2004.
43. K. Yu, V. Tresp, and A. Schwaighofer, "Learning Gaussian Processes from Multiple Tasks," Proc. 22nd Int'l Conf. Machine Learning, pp. 1012-1019, 2005.
44. H. Daumé III, "Bayesian Multitask Learning with Latent Hierarchies," Proc. 25th Conf. Uncertainty in Artificial Intelligence, pp. 135-142, 2009.