Inter-domain Cluster Mapping and GMCV Based Transformation for Domain Adaptation

  • Suranjana Samanta
  • Sukhendu Das
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

This paper describes an algorithm for a direct solution of domain adaptation (DA) to transform data in source domain to match the distribution in the target domain. This is achieved by formulating a transformation matrix based on the Geometric Mean of Co-Variances (GMCV), estimated from the covariance matrices of the data from both the domains. As a pre-processing step, we propose an iterative framework for clustering over data from both the domains, to produce an inter-domain mapping function of clusters. A closed form solution for direct DA is obtained from the GMCV formulation. Experimental results on real world datasets confirms the importance of clustering prior to transformation using GMCV for better classification accuracy. Results show the superior result of the proposed method of DA, when compared with a few state of the art methods.

Keywords

Training Sample Transformation Matrix Target Domain Domain Adaptation Transfer Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Sugiyama, M., Nakajima, S., Kashima, H., von Bünau, P., Kawanabe, M.: Direct importance estimation with model selection and its application to covariate shift adaptation. In: Neural Information Processing Systems, pp. 1962–1965 (2007)Google Scholar
  2. 2.
    Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: International Conference on Machine Learning, pp. 193–200 (2007)Google Scholar
  3. 3.
    Jiang, W., Zavesky, E., Fu Chang, S., Loui, A.: Cross-domain learning methods for high-level visual concept classification. In: International Conference on Image Processing, pp. 161–164 (2008)Google Scholar
  4. 4.
    Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive SVMs. In: International Conference on Multimedia, pp. 188–197 (2007)Google Scholar
  5. 5.
    Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Self-taught clustering. In: International Conference on Machine Learning, pp. 200–207 (2008)Google Scholar
  6. 6.
    Bhattacharya, I., Godbole, S., Joshi, S., Verma, A.: Cross-guided clustering: Transfer of relevant supervision across domains for improved clustering. In: International Conference on Data Mining, pp. 41–50 (2009)Google Scholar
  7. 7.
    Asuncion, A., Newman, D.H.: UCI machine learning repository (2007)Google Scholar
  8. 8.
    Shi, X., Fan, W., Ren, J.: Actively transfer domain knowledge. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 342–357. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman Divergences. Journal of Machine Learning Research 6, 1705–1749 (2005)MathSciNetMATHGoogle Scholar
  10. 10.
    Lawson, J.D., Lim, Y.: The geometric mean, matrices, metrics, and more. The American Mathematical Monthly 108(9), 797–812 (2001)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Duan, L., Xu, D., Tsang, I.W.H.: Domain adaptation from multiple sources: A domain-dependent regularization approach. IEEE Transaction Neural Network Learning System 23(3) (2012), http://vc.sce.ntu.edu.sg/transfer-learning-domain-adaptation/domain-adaptation-home.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Suranjana Samanta
    • 1
  • Sukhendu Das
    • 1
  1. 1.V.P. Lab, Dept. of CSEIIT MadrasIndia

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