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.
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References
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)
Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: International Conference on Machine Learning, pp. 193–200 (2007)
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)
Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive SVMs. In: International Conference on Multimedia, pp. 188–197 (2007)
Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Self-taught clustering. In: International Conference on Machine Learning, pp. 200–207 (2008)
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)
Asuncion, A., Newman, D.H.: UCI machine learning repository (2007)
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)
Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman Divergences. Journal of Machine Learning Research 6, 1705–1749 (2005)
Lawson, J.D., Lim, Y.: The geometric mean, matrices, metrics, and more. The American Mathematical Monthly 108(9), 797–812 (2001)
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
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Samanta, S., Das, S. (2013). Inter-domain Cluster Mapping and GMCV Based Transformation for Domain Adaptation. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_9
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DOI: https://doi.org/10.1007/978-3-642-45062-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45061-7
Online ISBN: 978-3-642-45062-4
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