{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:03:10Z","timestamp":1742925790265,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031234729"},{"type":"electronic","value":"9783031234736"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23473-6_7","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:36:12Z","timestamp":1672540572000},"page":"79-91","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Matrix Factorization and\u00a0Structure Preserving for\u00a0Domain Adaptation"],"prefix":"10.1007","author":[{"given":"Wenhao","family":"Shao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jigang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"7_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-319-11382-1_18","volume-title":"Information Access Evaluation. Multilinguality, Multimodality, and Interaction","author":"B Caputo","year":"2014","unstructured":"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\u2013211. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11382-1_18"},{"key":"7_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1007\/978-3-319-49409-8_37","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"G Csurka","year":"2016","unstructured":"Csurka, G., Chidlowskii, B., Clinchant, S., Michel, S.: Unsupervised domain adaptation with regularized domain instance denoising. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 458\u2013466. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_37"},{"key":"7_CR3","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 59:1\u201359:35 (2016)"},{"key":"7_CR4","unstructured":"Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR, pp. 2066\u20132073. IEEE Computer Society (2012)"},{"issue":"11","key":"7_CR5","doi-asserted-by":"publisher","first-page":"5316","DOI":"10.1109\/TIP.2018.2855421","volume":"27","author":"X Guo","year":"2018","unstructured":"Guo, X., Lin, Z.: Low-rank matrix recovery via robust outlier estimation. IEEE Trans. Image Process. 27(11), 5316\u20135327 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"7_CR6","doi-asserted-by":"publisher","first-page":"6103","DOI":"10.1109\/TIP.2019.2924174","volume":"28","author":"J Li","year":"2019","unstructured":"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\u20136115 (2019)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"7_CR7","doi-asserted-by":"publisher","first-page":"2144","DOI":"10.1109\/TCYB.2018.2820174","volume":"49","author":"J Li","year":"2019","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 (2019)","journal-title":"IEEE Trans. Cybern."},{"issue":"9","key":"7_CR8","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":"7_CR9","unstructured":"Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: NIPS, pp. 612\u2013620 (2011)"},{"key":"7_CR10","unstructured":"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\u2013105. JMLR.org (2015)"},{"key":"7_CR11","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: NeurIPS, pp. 1647\u20131657 (2018)"},{"issue":"7","key":"7_CR12","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1109\/TKDE.2013.97","volume":"26","author":"M Long","year":"2014","unstructured":"Long, M., Wang, J., Ding, G., Shen, D., Yang, Q.: Transfer learning with graph co-regularization. IEEE Trans. Knowl. Data Eng. 26(7), 1805\u20131818 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: ICCV, pp. 2200\u20132207. IEEE Computer Society (2013)","DOI":"10.1109\/ICCV.2013.274"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: CVPR, pp. 1410\u20131417. IEEE Computer Society (2014)","DOI":"10.1109\/CVPR.2014.183"},{"key":"7_CR15","unstructured":"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\u20132217. PMLR (2017)"},{"issue":"10","key":"7_CR16","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Roy, S., Siarohin, A., Sangineto, E., Bul\u00f2, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: CVPR, pp. 9471\u20139480. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00970"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Song, P., Ou, S., Zheng, W., Jin, Y., Zhao, L.: Speech emotion recognition using transfer non-negative matrix factorization. In: ICASSP, pp. 5180\u20135184. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7472665"},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2021.04.098","volume":"454","author":"J Sun","year":"2021","unstructured":"Sun, J., Wang, Z., Wang, W., Li, H., Sun, F.: Domain adaptation with geometrical preservation and distribution alignment. Neurocomputing 454, 152\u2013167 (2021)","journal-title":"Neurocomputing"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: CVPR, pp. 5385\u20135394. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Yu, H., Huang, M., Yang, Q.: Easy transfer learning by exploiting intra-domain structures. In: ICME, pp. 1210\u20131215. IEEE (2019)","DOI":"10.1109\/ICME.2019.00211"},{"key":"7_CR22","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: ACM Multimedia, pp. 402\u2013410. ACM (2018)","DOI":"10.1145\/3240508.3240512"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"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\u20131435. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00151"},{"issue":"2","key":"7_CR24","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/TIP.2015.2510498","volume":"25","author":"Y Xu","year":"2016","unstructured":"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\u2013863 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: CVPR, pp. 5150\u20135158. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.547"},{"key":"7_CR26","unstructured":"Zhang, L.: Transfer adaptation learning: A decade survey. CoRR abs\/1903.04687 (2019)"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23473-6_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T03:17:34Z","timestamp":1672543054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23473-6_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031234729","9783031234736"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23473-6_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"39","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2022a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cgs-network.org\/cgi22\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"139","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}