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Cited by 4,753

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Domain Adaptation via Transfer Component Analysis

Pan, Sinno Jialin, Tsang, Ivor W., Kwok, James T., Yang, Qiang·2011·IEEE Transactions on Neural Networks
DOI 10.1109/tnn.2010.2091281
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Abstract

Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.

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Research works printed but not linked: 35

  1. 10.1137/1.9781611970791 · DOI · Paper page
  2. belkin, 2006, manifold regularization: a geometric framework for learning from labeled and unlabeled examples, J Mach Learn Res, 7, 2399
  3. 10.1109/TNN.2004.841784 · doi:10.1109/tnn.2004.841784
  4. cristianini, 2002, Advances in Neural Information Processing Systems 14, 367
  5. 10.1145/1281192.1281218 · DOI · Paper page
  6. 10.1016/B978-1-55860-377-6.50048-7 · DOI · Paper page
  7. pan, 2009, domain adaptation via transfer component analysis, Proc 21st Int Joint Conf Artif Intell, 1187
  8. sorensen, 1996, Implicitly restarted Arnoldi/Lanczos method for large scale eigenvalue calculations
  9. pan, 2008, transfer learning via dimensionality reduction, Proc 23rd AAAI Conf Artif Intell, 677
  10. ando, 2005, a framework for learning predictive structures from multiple tasks and unlabeled data, J Mach Learn Res, 6, 1817
  11. 10.1103/PhysRevLett.103.214101 · DOI · Paper page
  12. 10.1109/TNN.2008.2000807 · DOI
  13. huang, 2007, Advances in Neural Information Processing Systems 19, 601
  14. sugiyama, 2008, Advances in Neural Information Processing Systems 20, 1433
  15. bickel, 2009, Discriminative learning under covariate shift, J Mach Learn Res, 10, 2137
  16. kanamori, 2009, a least-squares approach to direct importance estimation, J Mach Learn Res, 10, 1391
  17. 10.1016/j.neunet.2009.07.007 · DOI
  18. 10.1007/978-3-642-00599-2_17 · doi:10.1007/978-3-642-00599-2_17
  19. 10.1109/TNN.2008.2007961 · DOI
  20. 10.1109/72.914517 · DOI · Paper page
  21. 10.1109/TNN.2003.810608 · DOI
  22. 10.1162/089976698300017467 · DOI · Paper page
  23. ben-david, 2007, Advances in Neural Information Processing Systems 19, 137
  24. 10.7551/mitpress/9780262033589.001.0001 · DOI · Paper page
  25. daum, 2007, Frustratingly easy domain adaptation, Proc 40th Ann Meeting of the Assoc Comput Linguistics, 256
  26. song, 2008, Advances in Neural Information Processing Systems 20, 1385
  27. 10.1109/MIS.2008.4 · DOI
  28. fang, 2008, indoor location system based on discriminant-adaptive neural network in ieee 802.11 environments, IEEE Trans Neural Netw, 19, 1973, 10.1109/TNN.2008.2005494 · DOI
  29. 10.1109/ICDM.2008.49 · DOI
  30. smola, 2007, A Hilbert space embedding for distributions, 10th Int Conf Algorithmic Learning Theory, 13, 10.1007/978-3-540-75225-7_5 · DOI · Paper page
  31. 10.1137/1.9781611972795.34 · DOI
  32. gretton, 2005, Measuring statistical dependence with Hilbert-Schmidt norms, 10th Int Conf Algorithmic Learning Theory, 63, 10.1007/11564089_7 · DOI · Paper page
  33. gretton, 2007, A kernel method for the two-sample problem, Proc Conf Neural Inf Process Syst, 513
  34. 10.1145/1015330.1015345 · DOI · Paper page
  35. 10.1162/153244302760185252 · DOI

Cited by

Metadata

Authors
Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q.
Venue
IEEE Transactions on Neural Networks
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2011-02
Volume
22
Issue
2
Pages
199-210
ISSN
  • 1045-9227 (ISSN-L)
  • 1941-0093
DOI
10.1109/tnn.2010.2091281
OpenAlex
W304676660
Cited by
4,753
References
4
FWCI
42.85
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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