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Label Space Embedding of Manifold Alignment for Domain Adaption

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Abstract

In recent years, domain adaptation methods have aroused much interest in the machine learning community which transfer labeled information from the source domain to the target domain. However, most of the domain adaptation methods require that the source domain and target domain share the same features which may limit the applications of these approaches. In the paper, we propose a novel domain adaptation approach for heterogeneous source and target domains. Our approach reconstructs each sample point using the feature line distances between the sample point and training classes. Then we build the connections between the points from the source and target domains using the reconstructed vectors. Finally, the points from the target domain are projected to label space, preserving the local geometries of the target manifold and the connections between the source and target data. Different with the general domain adaptation approaches, the labels of the unlabeled target points can be predicted automatically without using the classifiers. Results of numerical experiments illustrate the effectiveness of the proposed approach.

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Notes

  1. The data set can be downloaded from http://vesicle.nsi.edu/users/patel/speech_database.html.

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Acknowledgements

The work of this author was supported in part by NSFC (61370006, 61673186), NSF of Fujian Province 2014J01237, and The Postgraduate Scientific Research Innovation Ability Training Plan Funding Projects of Huaqiao University (1511314004).

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Authors and Affiliations

  1. The School of Computer Science and Technology, Huaqiao University, Xiamen City, 361021, People’s Republic of China

    Jing Wang, Xueqing Li & Jixiang Du

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  1. Jing Wang
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  2. Xueqing Li
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  3. Jixiang Du
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Correspondence to Jing Wang.

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Wang, J., Li, X. & Du, J. Label Space Embedding of Manifold Alignment for Domain Adaption. Neural Process Lett 49, 375–391 (2019). https://doi.org/10.1007/s11063-018-9822-8

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  • DOI: https://doi.org/10.1007/s11063-018-9822-8

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