{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T23:50:42Z","timestamp":1769989842935,"version":"3.49.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T00:00:00Z","timestamp":1572825600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T00:00:00Z","timestamp":1572825600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s11760-019-01587-1","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T02:02:33Z","timestamp":1572832953000},"page":"609-616","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Joint local and statistical discriminant learning via feature alignment"],"prefix":"10.1007","volume":"14","author":[{"given":"Elahe","family":"Gholenji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4893-1272","authenticated-orcid":false,"given":"Jafar","family":"Tahmoresnezhad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,4]]},"reference":[{"key":"1587_CR1","unstructured":"Jhuo, I.H., Liu, D., Lee, D.T., Chang, S.F.: Robust visual domain adaptation with low-rank reconstruction. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, pp. 2168\u20132175. IEEE"},{"key":"1587_CR2","doi-asserted-by":"crossref","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: European conference on computer vision, September, pp. 213\u2013226. Springer, Berlin, Heidelberg (2010)","DOI":"10.1007\/978-3-642-15561-1_16"},{"issue":"2","key":"1587_CR3","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199\u2013210 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"key":"1587_CR4","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200\u20132207 (2013)","DOI":"10.1109\/ICCV.2013.274"},{"issue":"2","key":"1587_CR5","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s10115-016-0944-x","volume":"50","author":"J Tahmoresnezhad","year":"2017","unstructured":"Tahmoresnezhad, J., Hashemi, S.: Visual domain adaptation via transfer feature learning. Knowl. Inf. Syst. 50(2), 585\u2013605 (2017)","journal-title":"Knowl. Inf. Syst."},{"key":"1587_CR6","doi-asserted-by":"crossref","unstructured":"Fan, Y., Yan, G., Li, S., Song, S., Wang, W., Peng, X.: Transfer domain class clustering for unsupervised domain adaptation. In: International Conference on Electrical and Information Technologies for Rail Transportation, October, pp. 827\u2013835. Springer, Singapore (2017)","DOI":"10.1007\/978-981-10-7986-3_83"},{"key":"1587_CR7","unstructured":"Luo, L., Wang, X., Hu, S., Wang, C., Tang, Y., Chen, L.: Close yet distinctive domain adaptation (2017). arXiv preprint \narXiv:1704.04235"},{"key":"1587_CR8","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: 2017 IEEE International Conference on Data Mining (ICDM), November, pp. 1129\u20131134. IEEE (2017)","DOI":"10.1109\/ICDM.2017.150"},{"key":"1587_CR9","unstructured":"Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation (2017). arXiv preprint \narXiv:1705.05498"},{"key":"1587_CR10","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.neucom.2017.06.051","volume":"275","author":"J Liu","year":"2018","unstructured":"Liu, J., Li, J., Lu, K.: Coupled local-global adaptation for multi-source transfer learning. Neurocomputing 275, 247\u2013254 (2018)","journal-title":"Neurocomputing"},{"key":"1587_CR11","unstructured":"Luo, L., Chen, L., Hu, S., Lu, Y., Wang, X.: Discriminative and geometry aware unsupervised domain adaptation (2017). arXiv preprint \narXiv:1712.10042"},{"key":"1587_CR12","unstructured":"Gretton, A., Borgwardt, K., Rasch, M.J., Scholkopf, B., Smola, A.J.: A kernel method for the two-sample problem (2008). \narXiv:0805.2368"},{"issue":"4","key":"1587_CR13","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433\u2013459 (2010)","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"issue":"Nov","key":"1587_CR14","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7(Nov), 2399\u2013434 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"1587_CR15","doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality sensitive discriminant analysis. In: IJCAI, January, pp. 1713\u20131726 (2007)","DOI":"10.1109\/ICCV.2007.4408856"},{"key":"1587_CR16","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition. In: International conference on machine learning, January, pp. 647\u2013655 (2014)"},{"key":"1587_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Cheng, L., Peng, Y., Wen, Z., Ying, S.: Manifold alignment and distribution adaptation for unsupervised domain adaptation. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), July, pp. 688\u2013693. IEEE (2019)","DOI":"10.1109\/ICME.2019.00124"},{"issue":"6","key":"1587_CR18","doi-asserted-by":"publisher","first-page":"2144","DOI":"10.1109\/TCYB.2018.2820174","volume":"49","author":"J Li","year":"2018","unstructured":"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\u20132155 (2018)","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"1587_CR19","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1109\/TPAMI.2016.2599532","volume":"39","author":"M Ghifary","year":"2017","unstructured":"Ghifary, M., Balduzzi, D., Kleijn, W.B., Zhang, M.: Scatter component analysis: a unified framework for domain adaptation and domain generalization. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1414\u201330 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1587_CR20","unstructured":"Luo, L., Wang, X., Hu, S., Chen, L.: Robust data geometric structure aligned close yet discriminative domain adaptation (2017). \narXiv:1705.08620"},{"issue":"5","key":"1587_CR21","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1109\/TPAMI.2018.2832198","volume":"41","author":"J Liang","year":"2018","unstructured":"Liang, J., He, R., Sun, Z., Tan, T.: Aggregating randomized clustering-promoting invariant projections for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 41(5), 1027\u20131042 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"1587_CR22","doi-asserted-by":"publisher","first-page":"4260","DOI":"10.1109\/TIP.2018.2839528","volume":"27","author":"S Li","year":"2018","unstructured":"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\u20134273 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"1587_CR23","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097\u20131105 (2012)"},{"key":"1587_CR24","doi-asserted-by":"crossref","unstructured":"Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., van den Hengel, A.: When unsupervised domain adaptation meets tensor representations. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 599\u2013608 (2017)","DOI":"10.1109\/ICCV.2017.72"},{"key":"1587_CR25","doi-asserted-by":"crossref","unstructured":"Gholami, B., Pavlovic, V.: Punda: Probabilistic unsupervised domain adaptation for knowledge transfer across visual categories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3581\u20133590 (2017)","DOI":"10.1109\/ICCV.2017.387"},{"issue":"3","key":"1587_CR26","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1109\/TCYB.2016.2523538","volume":"47","author":"M Uzair","year":"2016","unstructured":"Uzair, M., Mian, A.: Blind domain adaptation with augmented extreme learning machine features. IEEE Trans. Cybern. 47(3), 651\u2013660 (2016)","journal-title":"IEEE Trans. Cybern."},{"key":"1587_CR27","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance (2014). arXiv preprint \narXiv:1412.3474"},{"key":"1587_CR28","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks (2015). arXiv preprint \narXiv:1502.02791"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-019-01587-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11760-019-01587-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-019-01587-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:26:21Z","timestamp":1604363181000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11760-019-01587-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,4]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["1587"],"URL":"https:\/\/doi.org\/10.1007\/s11760-019-01587-1","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,4]]},"assertion":[{"value":"11 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}