{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:24:33Z","timestamp":1781022273743,"version":"3.54.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00530-024-01339-3","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T10:01:56Z","timestamp":1715248916000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Visual transductive learning via iterative label correction"],"prefix":"10.1007","volume":"30","author":[{"given":"Samaneh","family":"Rezaei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahya","family":"Ahmadvand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jafar","family":"Tahmoresnezhad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"issue":"10","key":"1339_CR1","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"1339_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 1\u201340 (2016)","journal-title":"J. Big Data"},{"issue":"3","key":"1339_CR3","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s11760-019-01587-1","volume":"14","author":"E Gholenji","year":"2020","unstructured":"Gholenji, E., Tahmoresnezhad, J.: Joint local and statistical discriminant learning via feature alignment. SIViP 14(3), 609\u2013616 (2020)","journal-title":"SIViP"},{"issue":"4","key":"1339_CR4","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s42044-019-00037-y","volume":"2","author":"S Rezaei","year":"2019","unstructured":"Rezaei, S., Tahmoresnezhad, J.: Discriminative and domain invariant subspace alignment for visual tasks. Iran J. Comput. Sci. 2(4), 219\u2013230 (2019)","journal-title":"Iran J. Comput. Sci."},{"issue":"2","key":"1339_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."},{"issue":"2","key":"1339_CR6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/s11760-020-01745-w","volume":"15","author":"Shiva Noori Saray and Jafar Tahmoresnezhad","year":"2021","unstructured":"Shiva Noori Saray and Jafar Tahmoresnezhad: Joint distinct subspace learning and unsupervised transfer classification for visual domain adaptation. SIViP 15(2), 279\u2013287 (2021)","journal-title":"SIViP"},{"key":"1339_CR7","doi-asserted-by":"crossref","unstructured":"Zeng, H., Yue, Z., Kou, Z., Shang, L., Zhang, Y., Wang, D.: Unsupervised domain adaptation for covid-19 information service with contrastive adversarial domain mixup. In: International conference on advances in social networks analysis and mining (ASONAM), pages 159\u2013162 (2022)","DOI":"10.1109\/ASONAM55673.2022.10068580"},{"key":"1339_CR8","doi-asserted-by":"crossref","unstructured":"Rami, H., Ospici M., Lathuili\u00e8re, S.: Online unsupervised domain adaptation for person re-identification. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 3830\u20133839 (2022)","DOI":"10.1109\/CVPRW56347.2022.00428"},{"key":"1339_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, D., Lu, Y., Gao, C., Wang, W., Lu, J.: Progressive distribution alignment based on label correction for unsupervised domain adaptation. In 2021 IEEE international conference on multimedia and expo (ICME), pages 1\u20136. IEEE (2021)","DOI":"10.1109\/ICME51207.2021.9428235"},{"key":"1339_CR10","doi-asserted-by":"crossref","unstructured":"Alipour, N., Tahmoresnezhad, J.: Cross-domain pattern classification with heterogeneous distribution adaptation. Int. J. Mach. Learn Cybern. pages 1\u201317 (2022)","DOI":"10.1007\/s13042-022-01646-z"},{"key":"1339_CR11","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M., Sch\u00f6lkopf, B., Smola, A.J.: A kernel approach to comparing distributions. In: Proceedings of the national conference on artificial intelligence, volume\u00a022, page 1637. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999 (2007)"},{"key":"1339_CR12","doi-asserted-by":"publisher","first-page":"6110","DOI":"10.1109\/TIP.2020.2988175","volume":"29","author":"Y Li","year":"2020","unstructured":"Li, Y., Wei, H., Li, H., Dong, H., Zhang, B., Tian, Q.: Aligning discriminative and representative features: An unsupervised domain adaptation method for building damage assessment. IEEE Trans. Image Process. 29, 6110\u20136122 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"1339_CR13","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/TNNLS.2018.2868709","volume":"30","author":"P Wei","year":"2018","unstructured":"Wei, P., Ke, Y., Goh, C.K.: Feature analysis of marginalized stacked denoising autoenconder for unsupervised domain adaptation. IEEE Trans. Neural Netw. Learni. Syst. 30(5), 1321\u20131334 (2018)","journal-title":"IEEE Trans. Neural Netw. Learni. Syst."},{"key":"1339_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1859\u20131867 (2017)","DOI":"10.1109\/CVPR.2017.547"},{"key":"1339_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Davison, B.D.: Adversarial continuous learning in unsupervised domain adaptation. In: International conference on pattern recognition, pages 672\u2013687. Springer (2021)","DOI":"10.1007\/978-3-030-68790-8_52"},{"key":"1339_CR16","first-page":"3296","volume":"33","author":"C Chen","year":"2019","unstructured":"Chen, C., Chen, Z., Jiang, B., Jin, X.: Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation. Proc. AAAI Conf. Artif. Intell. 33, 3296\u20133303 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1339_CR17","doi-asserted-by":"crossref","unstructured":"Chen, C., Xie, W., Huang, W., Rong, Y., Ding, X., Huang, Y., Xu, T., Huang, J.: Progressive feature alignment for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 627\u2013636 (2019)","DOI":"10.1109\/CVPR.2019.00072"},{"key":"1339_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Davison, B.D.: Efficient pre-trained features and recurrent pseudo-labeling in unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 2719\u20132728 (2021)","DOI":"10.1109\/CVPRW53098.2021.00306"},{"key":"1339_CR19","unstructured":"Xie, S., Zheng, Z., Chen, L., Chen, C.: Learning semantic representations for unsupervised domain adaptation. In: International conference on machine learning, pages 5423\u20135432. PMLR, (2018)"},{"key":"1339_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ouyang, W., Li, W., Xu, D.: Collaborative and adversarial network for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3801\u20133809 (2018)","DOI":"10.1109\/CVPR.2018.00400"},{"key":"1339_CR21","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, pages 213\u2013226. Springer (2010)","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"1339_CR22","unstructured":"Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)"},{"key":"1339_CR23","unstructured":"Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression (pie) database. In: Proceedings of fifth IEEE international conference on automatic face gesture recognition, pages 53\u201358. IEEE (2002)"},{"key":"1339_CR24","doi-asserted-by":"crossref","unstructured":"Baktashmotlagh, M., Harandi, M.T., Lovell, B.C., Salzmann, M.: Unsupervised domain adaptation by domain invariant projection. In: Proceedings of the IEEE international conference on computer vision, pages 769\u2013776 (2013)","DOI":"10.1109\/ICCV.2013.100"},{"key":"1339_CR25","doi-asserted-by":"crossref","unstructured":"Cao, Y., Long, M., Wang, J.: Unsupervised domain adaptation with distribution matching machines. In: Proceedings of the AAAI conference on artificial intelligence, volume\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11792"},{"key":"1339_CR26","doi-asserted-by":"crossref","unstructured":"Wang, W., Wang, H., Zhang, C., Xu, F.: Transfer feature representation via multiple kernel learning. In: Twenty-Ninth AAAI conference on artificial intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9586"},{"key":"1339_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, H., Jia, K., Tan, M.: Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 5031\u20135040 (2019)","DOI":"10.1109\/CVPR.2019.00517"},{"key":"1339_CR28","first-page":"6243","volume":"34","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Breckon, T.: Unsupervised domain adaptation via structured prediction based selective pseudo-labeling. Proc. AAAI Confer. Artif. Intell. 34, 6243\u20136250 (2020)","journal-title":"Proc. AAAI Confer. Artif. Intell."},{"issue":"5","key":"1339_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-021-1010-8","volume":"16","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Wang, C., Xue, H., Chen, S.: Self-corrected unsupervised domain adaptation. Front. Compute. Sci. 16(5), 165323 (2022)","journal-title":"Front. Compute. Sci."},{"key":"1339_CR30","doi-asserted-by":"crossref","unstructured":"Tian, Q., Du, X.: A plug-and-play noise-label correction framework for unsupervised domain adaptation person re-identification. Visu. Comput., pages 1\u201312 (2023)","DOI":"10.1007\/s00371-023-03094-4"},{"key":"1339_CR31","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, C., Li, B., Yuan, Y.: Metacorrection: domain-aware meta loss correction for unsupervised domain adaptation in semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 3927\u20133936 (2021)","DOI":"10.1109\/CVPR46437.2021.00392"},{"key":"1339_CR32","doi-asserted-by":"crossref","unstructured":"Zandifar, M., Saray, S.N., Tahmoresnezhad, J.: Domain adaptation via bregman divergence minimization. Sci Iran, 10 (2021)","DOI":"10.24200\/sci.2021.51486.2210"},{"key":"1339_CR33","doi-asserted-by":"crossref","unstructured":"Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: International conference on machine learning, pages 2988\u20132997. PMLR (2017)","DOI":"10.1109\/CVPR.2018.00392"},{"key":"1339_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1859\u20131867 (2017)","DOI":"10.1109\/CVPR.2017.547"},{"issue":"9","key":"1339_CR35","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., Cheng, W.: 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."},{"issue":"3","key":"1339_CR36","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s13042-020-01200-9","volume":"12","author":"S Rezaei","year":"2021","unstructured":"Rezaei, S., Tahmoresnezhad, J., Solouk, V.: A transductive transfer learning approach for image classification. Int. J. Mach. Learn. Cybernet. 12(3), 747\u2013762 (2021)","journal-title":"Int. J. Mach. Learn. Cybernet."},{"key":"1339_CR37","doi-asserted-by":"publisher","first-page":"6243","DOI":"10.1609\/aaai.v34i04.6091","volume":"34","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Breckon, T.: Unsupervised domain adaptation via structured prediction based selective pseudo-labeling. In Proceedings of the AAAI conference on artificial intelligence 34, 6243\u20136250 (2020)","journal-title":"In Proceedings of the AAAI conference on artificial intelligence"},{"key":"1339_CR38","doi-asserted-by":"crossref","unstructured":"Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5552\u20135560 (2018)","DOI":"10.1109\/CVPR.2018.00582"},{"issue":"10","key":"1339_CR39","doi-asserted-by":"publisher","first-page":"3476","DOI":"10.1109\/TPAMI.2020.2985708","volume":"43","author":"J Gao","year":"2020","unstructured":"Gao, J., Tianzhu, Z., Changsheng, X.: Learning to model relationships for zero-shot video classification. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3476\u20133491 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"1339_CR40","first-page":"41646","volume":"32","author":"J Gao","year":"2021","unstructured":"Gao, J., Changsheng, X.: Learning video moment retrieval without a single annotated video. IEEE Trans. Circuits Syst. Video Technol. 32(3), 41646\u20131657 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1339_CR41","doi-asserted-by":"crossref","unstructured":"Gao, J., Mengyuan, C., Changsheng, X.: Vectorized evidential learning for weakly-supervised temporal action localization. IEEE Trans. Pattern Anal. Mach. Intell., (2023)","DOI":"10.1109\/TPAMI.2023.3311447"},{"key":"1339_CR42","doi-asserted-by":"crossref","unstructured":"Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: Proceedings of the 26th ACM international conference on Multimedia, pages 402\u2013410 (2018)","DOI":"10.1145\/3240508.3240512"},{"key":"1339_CR43","unstructured":"Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res., 7(11) (2006)"},{"key":"1339_CR44","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, pages 647\u2013655. PMLR (2014)"},{"issue":"6","key":"1339_CR45","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"issue":"4","key":"1339_CR46","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1109\/TNNLS.2018.2863240","volume":"30","author":"W-Y Deng","year":"2018","unstructured":"Deng, W.-Y., Lendasse, A., Ong, Y.-S., Tsang, I.W.-H., Chen, L., Zheng, Q.-H.: Domain adaption via feature selection on explicit feature map. IEEE Trans. Neural Netw. Learn Syst. 30(4), 1180\u20131190 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn Syst."},{"key":"1339_CR47","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Yu, H., Huang, M., Yang, Q.: Easy transfer learning by exploiting intra-domain structures. In 2019 IEEE international conference on multimedia and expo (ICME), pages 1210\u20131215. IEEE (2019)","DOI":"10.1109\/ICME.2019.00211"},{"issue":"16","key":"1339_CR48","doi-asserted-by":"publisher","first-page":"4367","DOI":"10.3390\/s20164367","volume":"20","author":"RK Sanodiya","year":"2020","unstructured":"Sanodiya, R.K., Yao, L.: A subspace based transfer joint matching with laplacian regularization for visual domain adaptation. Sensors 20(16), 4367 (2020)","journal-title":"Sensors"},{"key":"1339_CR49","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TIP.2019.2928630","volume":"29","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Song, S., Li, S., Cheng, W.: A graph embedding framework for maximum mean discrepancy-based domain adaptation algorithms. IEEE Trans. Image Process. 29, 199\u2013213 (2019)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"1339_CR50","doi-asserted-by":"publisher","first-page":"3545","DOI":"10.1007\/s00521-020-05228-4","volume":"33","author":"J Li","year":"2021","unstructured":"Li, J., Li, Z., L\u00fc, S.: Unsupervised double weighted domain adaptation. Neural Comput. Appl. 33(8), 3545\u20133566 (2021)","journal-title":"Neural Comput. Appl."},{"key":"1339_CR51","doi-asserted-by":"publisher","first-page":"139052","DOI":"10.1109\/ACCESS.2020.3012152","volume":"8","author":"S Zang","year":"2020","unstructured":"Zang, S., Cheng, Y., Wang, X., Qiang, Yu., Xie, G.-S.: Cross domain mean approximation for unsupervised domain adaptation. IEEE Access 8, 139052\u2013139069 (2020)","journal-title":"IEEE Access"},{"key":"1339_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107941","volume":"100","author":"K Shi","year":"2022","unstructured":"Shi, K., Liu, Z., Wenpeng, L., Weihua, O., Yang, C.: Unsupervised domain adaptation based on adaptive local manifold learning. Comput. Electr. Eng. 100, 107941 (2022)","journal-title":"Comput. Electr. Eng."},{"key":"1339_CR53","doi-asserted-by":"crossref","unstructured":"Sanodiya, R.K., Mathew, A., Mathew, J., Khushi, M.: Statistical and geometrical alignment using metric learning in domain adaptation. In: 2020 international joint conference on neural networks (IJCNN), pages 1\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206877"},{"key":"1339_CR54","doi-asserted-by":"crossref","unstructured":"Raab, C., Schleif, F.-M.: Low-rank subspace override for unsupervised domain adaptation. In: German conference on artificial intelligence (K\u00fcnstliche Intelligenz), pages 132\u2013147. Springer (2020)","DOI":"10.1007\/978-3-030-58285-2_10"},{"issue":"24","key":"1339_CR55","doi-asserted-by":"publisher","first-page":"18713","DOI":"10.1007\/s00500-020-05105-1","volume":"24","author":"RK Sanodiya","year":"2020","unstructured":"Sanodiya, R.K., Tiwari, M., Mathew, J., Saha, S., Saha, S.: A particle swarm optimization-based feature selection for unsupervised transfer learning. Soft. Comput. 24(24), 18713\u201318731 (2020)","journal-title":"Soft. Comput."},{"key":"1339_CR56","doi-asserted-by":"publisher","first-page":"87049","DOI":"10.1109\/ACCESS.2021.3087867","volume":"9","author":"F Tingting","year":"2021","unstructured":"Tingting, F., Li, Y.: Unsupervised domain adaptation based on pseudo-label confidence. IEEE Access 9, 87049\u201387057 (2021)","journal-title":"IEEE Access"},{"key":"1339_CR57","doi-asserted-by":"crossref","unstructured":"Fu, T.: Unsupervised domain adaptation based on the geography structural information. In: 2021 2nd international conference on big data and informatization education (ICBDIE), pages 553\u2013557. IEEE (2021)","DOI":"10.1109\/ICBDIE52740.2021.00131"},{"issue":"4","key":"1339_CR58","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1007\/s13042-021-01428-z","volume":"13","author":"NS Shiva","year":"2022","unstructured":"Shiva, N.S., Jafar, T.: Iterative joint classifier and domain adaptation for visual transfer learning. Int. J. Mach. Learn. Cybernet. 13(4), 947\u2013961 (2022)","journal-title":"Int. J. Mach. Learn. Cybernet."},{"key":"1339_CR59","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, X.-L.: Improving pseudo labels with intra-class similarity for unsupervised domain adaptation. Pattern Recognit., 138 (2023)","DOI":"10.1016\/j.patcog.2023.109379"},{"issue":"21","key":"1339_CR60","doi-asserted-by":"publisher","first-page":"25412","DOI":"10.1007\/s10489-023-04706-1","volume":"53","author":"RK Sanodiya","year":"2023","unstructured":"Sanodiya, R.K., Jose, B.R., Mathew, J.: Kernelized global-local discriminant information preservation for unsupervised domain adaptation. Appl. Intell. 53(21), 25412\u201325434 (2023)","journal-title":"Appl. Intell."},{"issue":"4","key":"1339_CR61","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s42044-023-00144-x","volume":"6","author":"M Zandifar","year":"2023","unstructured":"Zandifar, M., Rezaei, S., Tahmoresnezhad, J.: Unsupervised domain adaptation via transferred local fisher discriminant analysis. Iran J. Comput. Sci. 6(4), 345\u2013364 (2023)","journal-title":"Iran J. Comput. Sci."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01339-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01339-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01339-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T13:18:13Z","timestamp":1720185493000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01339-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,9]]},"references-count":61,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1339"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01339-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3304024\/v1","asserted-by":"object"}]},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,9]]},"assertion":[{"value":"28 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"145"}}