Abstract
For cross-domain tasks in real-world, a source domain and a target domain often have different marginal probability distribution and conditional probability distribution. To leverage the distribution difference between the source and target domain, domain adaptation has been applied in many fields. Unfortunately, as most of the existing domain adaptation methods only focus on eliminating the distribution discrepancy between the two domains, they do not make full use of the correlation information and data distribution structure between the two domains. In this paper, we put forward a novel domain adaptation method named correlated matching and structure learning (CMSL), which considers the association information between source and target domains, and extracts the feature representation and thus can learn the maximization correlation features between the two domains. Simultaneously, the class centroids of the source data are used to cluster the target data, and a local manifold self-learning strategy is introduced to the target domain to preserve the underlying structure of the data. Experimental results on six data benchmarks show that our proposed method achieves good classification performance and outperforms several state-of-the-art unsupervised domain adaptation methods.
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References
Zhang, L., Wang, S., Huang, G., Zuo, W., Yang, J., Zhang, D.: Manifold criterion guided transfer learning via intermediate domain generation. IEEE Trans. Neural Netw. Learn. Syst. 30(12), 3759–3773 (2019)
Yan, C., Li, L., Zhang, C., Liu, B., Zhang, Y., Dai, Q.: Cross-modality bridging and knowledge transferring for image understanding. IEEE Trans. Multimedia 21(10), 2675–2685 (2019)
Xiao, N., Zhang, L.: Dynamic weighted learning for unsupervised domain adaptation. In: CVPR, pp. 15237–15246 (2021)
Wei, G., Lan, C., Zeng, W., Chen, Z.: MetaAlign: coordinating domain alignment and classification for unsupervised domain adaptation. In: CVPR, pp. 16638–16648 (2021)
Wang, S., Zhang, L., Zuo, W., Zhang, B.: Class-specific reconstruction transfer learning for visual recognition across domains. IEEE Trans. Image Process. 29, 2424–2438 (2020)
Zhang, L., Zuo, W., Zhang, D.: LSDT: latent sparse domain transfer learning for visual adaptation. IEEE Trans. Image Process. 25(3), 1177–1191 (2016)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: CVPR, pp. 2200–2207 (2013)
Wang, J., Chen, Y., Hao, S.: Balanced distribution adaptation for transfer learning. In: ICDM, pp. 1129–1134 (2017)
Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: CVPR, pp. 1859–1867 (2017)
Zhang, L., Zhang, D.: Visual understanding via multi-feature shared learning with global consistency. IEEE Trans. Multimedia 18(2), 247–259 (2016)
Yan, H., Li, Z., Wang, Q., Li, P., Xu, Y., Zuo, W.: Weighted and class-specific maximum mean discrepancy for unsupervised domain adaptation. IEEE Trans. Multimedia 22(9), 2420–2433 (2020)
Li, L., Zhang, Z.: Semi-supervised domain adaptation by covariance matching. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2724–2739 (2019)
Zhang, L., Fu, J., Wang, S., Zhang, D., Dong, Z., Chen, C.P.: Guide subspace learning for unsupervised domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3374–3388 (2020)
Hotelling, H.: Relations between Two Sets of Variates. Biometrika (1936)
Wang, W., Lu, Y., Lai, Z.: Canonical correlation discriminative learning for domain adaptation. In: Bäck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12269, pp. 567–580. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58112-1_39
Hua, M., Lau, M., Pei, J., Wu, K.: Continuous K-means monitoring with low reporting cost in sensor networks. IEEE Trans. Knowl. Data Eng. 21(12), 1679–1691 (2009)
Tian, L., Tang, Y., Hu, L., Ren, Z., Zhang, W.: Domain Adaptation by class centroid matching and local manifold self-learning. IEEE Trans. Image Process. 29(10), 9703–9718 (2020)
Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: KDD, pp. 977–986 (2014)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR, pp. 2066–2073 (2012)
Li, S., Song, S., Huang, G., Ding, Z.: Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Trans. Image Process. 27(9), 4260–4273 (2018)
Wang, J., Chen, Y., Yu, H., Huang, M., Yang, Q.: Easy transfer learning by exploiting intra-domain structures. In: Proceedings of IEEE International Conference on Multimedia Expo, pp. 1210–1215, July 2019
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16
McAuley, J., Targett, C., Shi, Q., Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52 (2015)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62176162, 61976145, and 62076129), the Guangdong Basic and Applied Basic Research Foundation (2019A1515011493, 2021A1515011318), the China University Industry-University-Research Innovation Fund (2020HYA02013), the Shenzhen Municipal Science and Technology Innovation Council (JCYJ20190808113411274), and the Major Project of the New Generation of Artificial Intelligence of China (2018AAA0102900).
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Luo, X., Lu, Y., Wen, J., Lai, Z. (2022). Correlated Matching and Structure Learning for Unsupervised Domain Adaptation. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_6
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