{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:46:40Z","timestamp":1742914000568,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031189098"},{"type":"electronic","value":"9783031189104"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18910-4_48","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"601-613","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Coupled Learning for\u00a0Kernel Representation and\u00a0Graph Tensor in\u00a0Multi-view Subspace Clustering"],"prefix":"10.1007","author":[{"given":"Man-Sheng","family":"Chen","sequence":"first","affiliation":[]},{"given":"Chang-Dong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jian-Huang","family":"Lai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"48_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105482","volume":"194","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Wang, S., Zheng, F., Cen, Y.: Graph-regularized least squares regression for multi-view subspace clustering. Knowl. Based Syst. 194, 105482 (2020)","journal-title":"Knowl. Based Syst."},{"key":"48_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107441","volume":"106","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Xiao, X., Zhou, Y.: Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix. Pattern Recognit. 106, 107441 (2020)","journal-title":"Pattern Recognit."},{"key":"48_CR3","unstructured":"Cortes, C., Mohri, M., Rostamizadeh, A.: Learning non-linear combinations of kernels. In: NIPS, pp. 396\u2013404 (2009)"},{"key":"48_CR4","doi-asserted-by":"crossref","unstructured":"Gao, H., Nie, F., Li, X., Huang, H.: Multi-view subspace clustering. In: ICCV, pp. 4238\u20134246 (2015)","DOI":"10.1109\/ICCV.2015.482"},{"key":"48_CR5","doi-asserted-by":"crossref","unstructured":"Gao, Q., Xia, W., Wan, Z., Xie, D., Zhang, P.: Tensor-SVD based graph learning for multi-view subspace clustering. In: AAAI, pp. 3930\u20133937 (2020)","DOI":"10.1609\/aaai.v34i04.5807"},{"issue":"3","key":"48_CR6","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.laa.2010.09.020","volume":"435","author":"ME Kilmer","year":"2011","unstructured":"Kilmer, M.E., Martin, C.D.: Factorization strategies for third-order tensors. Linear Algebra Appl. 435(3), 641\u2013658 (2011)","journal-title":"Linear Algebra Appl."},{"issue":"1","key":"48_CR7","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1137\/110837711","volume":"34","author":"ME Kilmer","year":"2013","unstructured":"Kilmer, M.E., Braman, K.S., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging. SIAM J. Matrix Anal. Appl. 34(1), 148\u2013172 (2013)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"48_CR8","unstructured":"Kumar, A., Daum\u00e9 III, H.: A co-training approach for multi-view spectral clustering. In: ICML, pp. 393\u2013400 (2011)"},{"key":"48_CR9","unstructured":"Kumar, A., Rai, P., Daum\u00e9 III, H.: Co-regularized multi-view spectral clustering. In: NIPS, pp. 1413\u20131421 (2011)"},{"key":"48_CR10","unstructured":"Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: NIPS, pp. 849\u2013856 (2002)"},{"issue":"3","key":"48_CR11","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1109\/TIP.2017.2754939","volume":"27","author":"F Nie","year":"2018","unstructured":"Nie, F., Cai, G., Li, J., Li, X.: Auto-weighted multi-view learning for image clustering and semi-supervised classification. IEEE Trans. Image Process. 27(3), 1501\u20131511 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"48_CR12","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.1109\/JSAC.2020.3041396","volume":"39","author":"Z Ren","year":"2021","unstructured":"Ren, Z., Mukherjee, M., Bennis, M., Lloret, J.: Multikernel clustering via non-negative matrix factorization tailored graph tensor over distributed networks. IEEE J. Sel. Areas Commun. 39(7), 1946\u20131956 (2021)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"48_CR13","doi-asserted-by":"crossref","unstructured":"Ren, Z., Sun, Q., Wei, D.: Multiple kernel clustering with kernel k-means coupled graph tensor learning. In: AAAI, pp. 9411\u20139418 (2021)","DOI":"10.1609\/aaai.v35i11.17134"},{"key":"48_CR14","doi-asserted-by":"crossref","unstructured":"Tzortzis, G., Likas, A.: Kernel-based weighted multi-view clustering. In: ICDM, pp. 675\u2013684 (2012)","DOI":"10.1109\/ICDM.2012.43"},{"issue":"6","key":"48_CR15","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TKDE.2019.2903810","volume":"32","author":"H Wang","year":"2020","unstructured":"Wang, H., Yang, Y., Liu, B.: GMC: graph-based multi-view clustering. IEEE Trans. Knowl. Data Eng. 32(6), 1116\u20131129 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"48_CR16","doi-asserted-by":"crossref","unstructured":"Wang, X., Guo, X., Lei, Z., Zhang, C., Li, S.Z.: Exclusivity-consistency regularized multi-view subspace clustering. In: CVPR, pp. 1\u20139 (2017)","DOI":"10.1109\/CVPR.2017.8"},{"issue":"12","key":"48_CR17","doi-asserted-by":"publisher","first-page":"5910","DOI":"10.1109\/TIP.2019.2916740","volume":"28","author":"J Wu","year":"2019","unstructured":"Wu, J., Lin, Z., Zha, H.: Essential tensor learning for multi-view spectral clustering. IEEE Trans. Image Process. 28(12), 5910\u20135922 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"48_CR18","doi-asserted-by":"crossref","unstructured":"Xia, R., Pan, Y., Du, L., Yin, J.: Robust multi-view spectral clustering via low-rank and sparse decomposition. In: AAAI, pp. 2149\u20132155 (2014)","DOI":"10.1609\/aaai.v28i1.8950"},{"issue":"11","key":"48_CR19","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1007\/s11263-018-1086-2","volume":"126","author":"Y Xie","year":"2018","unstructured":"Xie, Y., Tao, D., Zhang, W., Liu, Y., Zhang, L., Qu, Y.: On unifying multi-view self-representations for clustering by tensor multi-rank minimization. Int. J. Comput. Vis. 126(11), 1157\u20131179 (2018)","journal-title":"Int. J. Comput. Vis."},{"issue":"1","key":"48_CR20","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TPAMI.2018.2877660","volume":"42","author":"C Zhang","year":"2020","unstructured":"Zhang, C., Fu, H., Hu, Q., Cao, X., Xie, Y., Tao, D., Xu, D.: Generalized latent multi-view subspace clustering. IEEE Trans. Pattern Anal. Mach. Intell. 42(1), 86\u201399 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, C., Fu, H., Liu, S., Liu, G., Cao, X.: Low-rank tensor constrained multiview subspace clustering. In: ICCV, pp. 1582\u20131590 (2015)","DOI":"10.1109\/ICCV.2015.185"},{"key":"48_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, C., Hu, Q., Fu, H., Zhu, P., Cao, X.: Latent multi-view subspace clustering. In: CVPR, pp. 4279\u20134287 (2017)","DOI":"10.1109\/CVPR.2017.461"},{"key":"48_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, G.Y., Zhou, Y.R., He, X.Y., Wang, C.D., Huang, D.: One-step kernel multi-view subspace clustering. Knowledge-Based Systems 189 (2020)","DOI":"10.1016\/j.knosys.2019.105126"},{"key":"48_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.E.: Novel methods for multilinear data completion and de-noising based on tensor-svd. In: CVPR, pp. 3842\u20133849 (2014)","DOI":"10.1109\/CVPR.2014.485"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18910-4_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:39:00Z","timestamp":1666827540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18910-4_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189098","9783031189104"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18910-4_48","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":"27 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","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":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","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":"233","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":"41% - 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":"3.03","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":"3.35","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}