{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:26:24Z","timestamp":1760239584013,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T00:00:00Z","timestamp":1606953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806068, 466 71901001, 91546108"],"award-info":[{"award-number":["61806068, 466 71901001, 91546108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Special Science and Technology Project of Anhui Province, China","award":["201903a05020020"],"award-info":[{"award-number":["201903a05020020"]}]},{"name":"Program for Outstanding Young Teachers in Higher Education Institutions of Anhui Province, China","award":["gxyq2020103"],"award-info":[{"award-number":["gxyq2020103"]}]},{"name":"Key Natural Science Project of College of Information Engineering, Fuyang Normal University, China","award":["FXG2020ZZ01"],"award-info":[{"award-number":["FXG2020ZZ01"]}]},{"name":"Natural Science Foundation of Anhui Province, China","award":["1708085MG169, 2008085QA16"],"award-info":[{"award-number":["1708085MG169, 2008085QA16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Domain adaptation manages to learn a robust classifier for target domain, using the source domain, but they often follow different distributions. To bridge distribution shift between the two domains, most of previous works aim to align their feature distributions through feature transformation, of which optimal transport for domain adaptation has attract researchers\u2019 interest, as it can exploit the local information of the two domains in the process of mapping the source instances to the target ones by minimizing Wasserstein distance between their feature distributions. However, it may weaken the feature discriminability of source domain, thus degrade domain adaptation performance. To address this problem, this paper proposes a two-stage feature-based adaptation approach, referred to as optimal transport with dimensionality reduction (OTDR). In the first stage, we apply the dimensionality reduction with intradomain variant maximization but source intraclass compactness minimization, to separate data samples as much as possible and enhance the feature discriminability of the source domain. In the second stage, we leverage optimal transport-based technique to preserve the local information of the two domains. Notably, the desirable properties in the first stage can mitigate the degradation of feature discriminability of the source domain in the second stage. Extensive experiments on several cross-domain image datasets validate that OTDR is superior to its competitors in classification accuracy.<\/jats:p>","DOI":"10.3390\/sym12121994","type":"journal-article","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T20:09:40Z","timestamp":1607026180000},"page":"1994","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimal Transport with Dimensionality Reduction for Domain Adaptation"],"prefix":"10.3390","volume":"12","author":[{"given":"Ping","family":"Li","sequence":"first","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei 230009, China"},{"name":"School of Information Engineering, Fuyang Normal University, Fuyang 236041, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China"}]},{"given":"Zhiwei","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei 230009, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China"}]},{"given":"Xuhui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei 230009, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China"}]},{"given":"Juan","family":"Song","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei 230009, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China"}]},{"given":"Wenying","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei 230009, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/978-3-030-30581-9_7","article-title":"Using Neural Networks for Classification of the Changes in the EEG Signal Based on Facial Expressions","volume":"Volume 852","author":"Kacprzyk","year":"2020","journal-title":"Analysis and Classification of EEG Signals for Brain\u2014Computer Interfaces"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Paszkiel, S. (2020, January 18\u201320). The use of facial expressions identified from the level of the EEG signal for controlling a mobile vehicle based on a state machine. Proceedings of the Conference on Automation, Warsaw, Poland.","DOI":"10.1007\/978-3-030-40971-5_21"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Paszkiel, S., Dobrakowski, P., and \u0141ysiak, A. (2020). The impact of different sounds on stress level in the context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study. Brain Sci., 10.","DOI":"10.3390\/brainsci10100728"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Paszkiel, S., and Sikora, M. (2019, January 27\u201329). The use of brain-computer interface to control unmanned aerial vehicle. Proceedings of the Conference on Automation, Warsaw, Poland.","DOI":"10.1007\/978-3-030-13273-6_54"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gopalan, A.B., Li, R., and Chellappa, R. (2011, January 6\u201313). Domain adaptation for object recognition: An unsupervised approach. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ins.2017.08.034","article-title":"Domain class consistency based transfer learning for image classification across domains","volume":"418","author":"Zhang","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_7","unstructured":"Griffin, G., Holub, A., and Perona, P. (2006, November 15). Caltech-256 Object Category Dataset, Technical Report 7694 Caltech. Available online: http:\/\/www.vision.caltech.edu\/archive.html."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dai, Y., Zhang, J., Yuan, S., and Xu, Z. (2019, January 8\u201311). A two-stage multi-task learning-based method for selective unsupervised domain adaptation. Proceedings of the International Conference on Data Mining Workshops, Beijing, China.","DOI":"10.1109\/ICDMW.2019.00126"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dai, W., Yang, Q., Xue, G.R., and Yu, Y. (2007, January 20\u201324). Boosting for transfer learning. Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, USA.","DOI":"10.1145\/1273496.1273521"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, J., Gretton, A., Borgwardt, K., Sch\u00f6lkopf, B., and Smola, A.J. (2007). Correcting sample selection bias by unlabeled data. Advances in Neural Information Processing Systems, Proceedings of the Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 3\u20136 December 2007, MIT Press.","DOI":"10.7551\/mitpress\/7503.003.0080"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3962","DOI":"10.1016\/j.patcog.2012.04.014","article-title":"On minimum distribution discrepancy support vector machine for domain adaptation","volume":"45","author":"Tao","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TKDE.2013.111","article-title":"Adaptation regularization: A general framework for transfer learning","volume":"26","author":"Long","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","unstructured":"Gong, B., Shi, Y., Sha, F., and Grauman, K. (2012, January 16\u201321). Geodesic flow kernel for unsupervised domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fernando, B., Habrard, A., Sebban, M., and Tuytelaars, T. (2013, January 1\u20138). Unsupervised visual domain adaptation using subspace alignment. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.368"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., and Yu, P.S. (2013, January 23\u201328). Transfer feature learning with joint distribution adaptation. Proceedings of the IEEE International Conference on Computer Vision, Portland, OR, USA.","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Courty, N., Flamary, R., and Tuia, D. (2014, January 15\u201319). Domain adaptation with regularized optimal transport. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France.","DOI":"10.1007\/978-3-662-44848-9_18"},{"key":"ref_19","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","unstructured":"Long, M., Zhu, H., Wang, J., and Jordan, M.I. (2017, January 6\u201311). Deep transfer learning with joint adaptation networks. Proceedings of the Conference on Machine Learning, Sydney, Australia."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.engappai.2018.05.001","article-title":"Sample-to-sample correspondence for unsupervised domain adaptation","volume":"73","author":"Das","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s10115-016-0944-x","article-title":"Visual domain adaptation via transfer feature learning","volume":"50","author":"Tahmoresnezhad","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4260","DOI":"10.1109\/TIP.2018.2839528","article-title":"Domain invariant and class discriminative feature learning for visual domain adaptation","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Baktashmotlagh, M., Harandi, M.T., Lovell, B.C., and Salzmann, M. (2013, January 23\u201328). Unsupervised domain adaptation by domain invariant projection. Proceedings of the IEEE International Conference on Computer Vision, Portland, OR, USA.","DOI":"10.1109\/ICCV.2013.100"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., and Saenko, K. (2016, January 12\u201317). Return of frustratingly easy domain adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1109\/TPAMI.2016.2615921","article-title":"Optimal transport for domain adaptation","volume":"39","author":"Courty","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1287\/mnsc.5.1.1","article-title":"On the translocation of masses","volume":"5","author":"Kantorovich","year":"1958","journal-title":"Manag. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, W., and Ogunbona, P. (2017, January 24\u201330). Joint geometrical and statistical alignment for visual domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA.","DOI":"10.1109\/CVPR.2017.547"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6103","DOI":"10.1109\/TIP.2019.2924174","article-title":"Locality preserving joint transfer for domain adaptation","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1109\/TCYB.2018.2820174","article-title":"Transfer independently together: A generalized framework for domain adaptation","volume":"49","author":"Li","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_32","first-page":"428","article-title":"Regularized discrete optimal transport","volume":"7","author":"Ferradans","year":"2013","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rabin, J., Ferradans, S., and Papadakis, N. (2014, January 24\u201327). Adaptive color transfer with relaxed optimal transport. Proceedings of the IEEE International Conference on Image Processing, Columbus, OH, USA.","DOI":"10.1109\/ICIP.2014.7025983"},{"key":"ref_34","unstructured":"Cuturi, M. (2013, January 5\u201310). Sinkhorn distances: Lightspeed computation of optimal transport. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Courty, N., Flamary, R., Habrard, A., and Rakotomamonjy, A. (2017). Joint distribution optimal transportation for domain adaptation. Advances in Neural Information Processing Systems, Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, 4\u20139 December 2017, MIT Press.","DOI":"10.1109\/TPAMI.2016.2615921"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1109\/TPAMI.2019.2903050","article-title":"Optimal transport in reproducing kernel Hilbert spaces: Theory and applications","volume":"42","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1109\/TNNLS.2019.2909737","article-title":"G-Softmax: Improving intraclass compactness and interclass separability of features","volume":"31","author":"Luo","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10589-007-9083-3","article-title":"A generalized conditional gradient method and its connection to an iterative shrinkage method","volume":"42","author":"Bredies","year":"2009","journal-title":"Comput. Optim. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Saenko, K., Kulis, B., Fritz, M., and Darrell, T. (2010, January 5\u201311). Adapting visual category models to new domains. Proceedings of the European Conference on Computer Vision, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref_40","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2014, January 22\u201324). Decaf: A deep convolutional activation feature for generic visual recognition. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_41","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S. (2017, January 24\u201330). Deep hashing network for unsupervised domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA.","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/TCSS.2020.3001517","article-title":"Enhanced subspace distribution matching for fast visual domain adaptation","volume":"7","author":"Kang","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_44","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M. (2015, January 7\u20139). Learning transferable features with deep adaptation networks. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ouyang, W., Li, W., and Xu, D. (2018, January 18\u201322). Collaborative and adversarial network for unsupervised domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00400"},{"key":"ref_46","unstructured":"Long, M., Cao, Z., Wang, J., and Jordan, M.I. (2018). Conditional adversarial domain adaptation. Advances in Neural Information Processing Systems, Proceeding of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montr\u00e9al, QC, Canada, 2\u20138 December 2018, MIT Press."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.neunet.2020.03.025","article-title":"DART: Domain-adversarial residual-transfer networks for unsupervised cross-domain image classification","volume":"127","author":"Fang","year":"2020","journal-title":"Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neunet.2019.07.010","article-title":"Multi-representation adaptation network for cross-domain image classification","volume":"119","author":"Zhu","year":"2019","journal-title":"Neural Netw."},{"key":"ref_49","unstructured":"Liu, H., Long, M., Wang, J., and Jordan, M.I. (2019, January 9\u201315). Transferable adversarial training: A general approach to adapting deep classifiers. Proceedings of the International Conference on Machine Learning, Berkeley, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neunet.2020.06.016","article-title":"Learning explicitly transferable representations for domain adaptation","volume":"130","author":"Jing","year":"2020","journal-title":"Neural Netw."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ins.2019.12.005","article-title":"Hybrid adversarial network for unsupervised domain adaptation","volume":"514","author":"Zhang","year":"2020","journal-title":"Inf. Sci."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/12\/1994\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:41:05Z","timestamp":1760179265000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/12\/1994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,3]]},"references-count":51,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["sym12121994"],"URL":"https:\/\/doi.org\/10.3390\/sym12121994","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,12,3]]}}}