Abstract
Domain adaptation aims to learn robust classifiers for the target domain by transferring knowledge from the labeled source domain. However, most of the existing studies emphasize learning domain-invariant feature representations by employing distribution alignment on the feature space, which ignores the influence of data noise and structure knowledge. To address these issues, we propose a new domain adaptation approach, which can effectively reduce the impact of data noise and simultaneously thoroughly exploit the manifold data structures to transfer discriminative knowledge. Specifically, we jointly model matrix factorization, maximum entropy, distribution alignment in a unified framework, which can effectively alleviate the negative transfer of the outliers. Furthermore, we devise graph dual regularization to thoroughly explore the intrinsic manifold data structures, which can significantly reduce structure discrepancy across domains. Experimental results on various domain adaptation tasks demonstrate the superiority of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Caputo, B., et al.: ImageCLEF 2014: overview and analysis of the results. In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 192–211. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11382-1_18
Csurka, G., Chidlowskii, B., Clinchant, S., Michel, S.: Unsupervised domain adaptation with regularized domain instance denoising. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 458–466. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_37
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 59:1–59:35 (2016)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR, pp. 2066–2073. IEEE Computer Society (2012)
Guo, X., Lin, Z.: Low-rank matrix recovery via robust outlier estimation. IEEE Trans. Image Process. 27(11), 5316–5327 (2018)
Li, J., Jing, M., Lu, K., Zhu, L., Shen, H.T.: Locality preserving joint transfer for domain adaptation. IEEE Trans. Image Process. 28(12), 6103–6115 (2019)
Li, J., Lu, K., Huang, Z., Zhu, L., Shen, H.T.: Transfer independently together: a generalized framework for domain adaptation. IEEE Trans. Cybern. 49(6), 2144–2155 (2019)
Li, S., Song, S., Huang, G., Ding, Z., Wu, C.: Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Trans. Image Process. 27(9), 4260–4273 (2018)
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: NIPS, pp. 612–620 (2011)
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: ICML. JMLR Workshop and Conference Proceedings, vol. 37, pp. 97–105. JMLR.org (2015)
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: NeurIPS, pp. 1647–1657 (2018)
Long, M., Wang, J., Ding, G., Shen, D., Yang, Q.: Transfer learning with graph co-regularization. IEEE Trans. Knowl. Data Eng. 26(7), 1805–1818 (2014)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: ICCV, pp. 2200–2207. IEEE Computer Society (2013)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: CVPR, pp. 1410–1417. IEEE Computer Society (2014)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: ICML. Proceedings of Machine Learning Research, vol. 70, pp. 2208–2217. PMLR (2017)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Roy, S., Siarohin, A., Sangineto, E., Bulò, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: CVPR, pp. 9471–9480. Computer Vision Foundation/IEEE (2019)
Song, P., Ou, S., Zheng, W., Jin, Y., Zhao, L.: Speech emotion recognition using transfer non-negative matrix factorization. In: ICASSP, pp. 5180–5184. IEEE (2016)
Sun, J., Wang, Z., Wang, W., Li, H., Sun, F.: Domain adaptation with geometrical preservation and distribution alignment. Neurocomputing 454, 152–167 (2021)
Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: CVPR, pp. 5385–5394. IEEE Computer Society (2017)
Wang, J., Chen, Y., Yu, H., Huang, M., Yang, Q.: Easy transfer learning by exploiting intra-domain structures. In: ICME, pp. 1210–1215. IEEE (2019)
Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: ACM Multimedia, pp. 402–410. ACM (2018)
Xu, R., Li, G., Yang, J., Lin, L.: Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation. In: ICCV, pp. 1426–1435. IEEE (2019)
Xu, Y., Fang, X., Wu, J., Li, X., Zhang, D.: Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans. Image Process. 25(2), 850–863 (2016)
Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: CVPR, pp. 5150–5158. IEEE Computer Society (2017)
Zhang, L.: Transfer adaptation learning: A decade survey. CoRR abs/1903.04687 (2019)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62172109 and Grant 62072118, in part by the Natural Science Foundation of Guangdong Province under Grant 2022A1515010322, in part by the High-Level Talents Programme of Guangdong Province under Grant 2017GC010556, and in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021B1515120010.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shao, W., Chen, H., Meng, M., Wu, J. (2022). Joint Matrix Factorization and Structure Preserving for Domain Adaptation. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-23473-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23472-9
Online ISBN: 978-3-031-23473-6
eBook Packages: Computer ScienceComputer Science (R0)