Abstract
In this paper, we propose a transfer domain class clustering (TDCC) algorithm to address the unsupervised domain adaptation problem, in which the training data (source domain) and the test data (target domain) follow different distributions. TDCC aims to derive new feature representations for source and target in a latent subspace to simultaneously reduce the distribution distance between two domains, which helps transfer the source knowledge to the target domain effectively, and enhance the class discriminativeness of data as much as possible by minimizing the intra-class variations, which can benefit the final classification a lot. The effectiveness of TDCC is verified by comprehensive experiments on several cross-domain datasets, and the results demonstrate that TDCC is superior to the competitive algorithms.
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Acknowledgements
This research is supported by the CRRC Major Scientific Projects under Grant No. 2106CKZ206-1 and National Key R&D Program under Grant No. 2016YFB1200203.
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Fan, Y., Yan, G., Li, S., Song, S., Wang, W., Peng, X. (2018). Transfer Domain Class Clustering for Unsupervised Domain Adaptation. In: Jia, L., Qin, Y., Suo, J., Feng, J., Diao, L., An, M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering, vol 482. Springer, Singapore. https://doi.org/10.1007/978-981-10-7986-3_83
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DOI: https://doi.org/10.1007/978-981-10-7986-3_83
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