{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T02:03:40Z","timestamp":1768701820157,"version":"3.49.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"17-18","license":[{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s10489-024-05616-6","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T17:02:04Z","timestamp":1719853324000},"page":"8545-8562","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Consensus representation-driven structured graph learning for multi-view clustering"],"prefix":"10.1007","volume":"54","author":[{"given":"Zhibin","family":"Gu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-9358","authenticated-orcid":false,"given":"Songhe","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiazheng","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ximing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"5616_CR1","unstructured":"Macqueen J (1965) Some methods for classification and analysis of multivariate observations. In: Proceedings of berkeley symposium on mathematical statistics and probability, pp 281\u2013297"},{"key":"5616_CR2","unstructured":"Ng AY, Jordan MI, Weiss Y, (2002) On spectral clustering: analysis and an algorithm. In: Proceedings of the international conference on neural information processing systems, pp 849\u2013856"},{"key":"5616_CR3","doi-asserted-by":"crossref","unstructured":"Elhamifar E, Vidal R, (2009) Sparse subspace clustering. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition workshops, pp 2790\u20132797","DOI":"10.1109\/CVPRW.2009.5206547"},{"issue":"1","key":"5616_CR4","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","volume":"35","author":"G Liu","year":"2013","unstructured":"Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171\u2013184","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"5616_CR5","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91\u2013110","journal-title":"Int J Comput Vis"},{"key":"5616_CR6","first-page":"428","volume":"3952","author":"N Dalal","year":"2006","unstructured":"Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. Proceedings of IEEE European conference on computer vision 3952:428\u2013441","journal-title":"Proceedings of IEEE European conference on computer vision"},{"issue":"7","key":"5616_CR7","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971\u2013987","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"5616_CR8","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 (2020) Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell 42(1):86\u201399","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5616_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2020.107524","volume":"108","author":"R Fan","year":"2020","unstructured":"Fan R, Luo T, Zhuge W, Qiang S, Hou C (2020) Multi-view subspace learning via bidirectional sparsity. Pattern Recog 108:1\u201311","journal-title":"Pattern Recog"},{"key":"5616_CR10","unstructured":"Xiao C, Nie F, Huang H, Kamangar F (2011) Heterogeneous image feature integration via multi-modal spectral clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1977\u20131984"},{"key":"5616_CR11","doi-asserted-by":"crossref","unstructured":"Nie F, Jing L, Li X (2017) Self-weighted multiview clustering with multiple graphs. In: Proceedings of the international joint conference on artificial intelligence, pp 2564\u20132570","DOI":"10.24963\/ijcai.2017\/357"},{"key":"5616_CR12","doi-asserted-by":"crossref","unstructured":"Li Y, Nie F, Huang H, Huang J (2015) Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the AAAI conference on artificial intelligence, pp 2750\u20132756","DOI":"10.1609\/aaai.v29i1.9598"},{"issue":"1","key":"5616_CR13","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1109\/TPAMI.2020.3011148","volume":"44","author":"X Li","year":"2022","unstructured":"Li X, Zhang H, Wang R, Nie F (2022) Multi-view clustering: a scalable and parameter-free bipartite graph fusion method. IEEE Trans Pattern Anal Mach Intell 44(1):330\u2013344","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5616_CR14","doi-asserted-by":"crossref","unstructured":"Tang C, Liu X, Zhu X, Zhu E, Luo Z, Wang L, Gao W (2020) Cgd: multi-view clustering via cross-view graph diffusion. In: Proceedings of the AAAI conference on artificial intelligence, pp 5924\u20135931","DOI":"10.1609\/aaai.v34i04.6052"},{"issue":"11","key":"5616_CR15","doi-asserted-by":"publisher","first-page":"4214","DOI":"10.1109\/TCSVT.2020.3049005","volume":"31","author":"Y Hu","year":"2021","unstructured":"Hu Y, Song Z, Wang B, Gao J, Sun Y, Yin B (2021) Akm3c: adaptive k-multiple-means for multi-view clustering. IEEE Trans Circ Syst Vid Technol 31(11):4214\u20134226","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"issue":"9","key":"5616_CR16","doi-asserted-by":"publisher","first-page":"6200","DOI":"10.1109\/TCSVT.2022.3162575","volume":"32","author":"B Yang","year":"2022","unstructured":"Yang B, Zhang X, Lin Z, Nie F, Chen B, Wang F (2022) Efficient and robust multiview clustering with anchor graph regularization. IEEE Trans Circ Syst Vid Technol 32(9):6200\u20136213","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"issue":"8","key":"5616_CR17","doi-asserted-by":"publisher","first-page":"5307","DOI":"10.1109\/TCSVT.2022.3143848","volume":"32","author":"G Jiang","year":"2022","unstructured":"Jiang G, Peng J, Wang H, Mi Z, Fu X (2022) Tensorial multi-view clustering via low-rank constrained high-order graph learning. IEEE Trans Circ Syst Vid Technol 32(8):5307\u20135318","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"key":"5616_CR18","doi-asserted-by":"crossref","unstructured":"Liang W, Liu X, Zhou S, Liu J, Wang S, Zhu E (2022) Robust graph-based multi-view clustering. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 7462\u20137469","DOI":"10.1609\/aaai.v36i7.20710"},{"key":"5616_CR19","unstructured":"Kumar A, Iii HD (2011) A co-training approach for multi-view spectral clustering. In: Proceedings of international conference on international conference on machine learning, pp 393\u2013400"},{"key":"5616_CR20","doi-asserted-by":"crossref","unstructured":"Lee CK, Liu TL (2016) Guided co-training for multi-view spectral clustering. In: Proceedings of IEEE international conference on image processing, pp 4042\u20134046","DOI":"10.1109\/ICIP.2016.7533119"},{"issue":"5","key":"5616_CR21","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1109\/TPAMI.2011.255","volume":"34","author":"S Yu","year":"2012","unstructured":"Yu S, Tranchevent L, Liu X, Glanzel W, Suykens JAK, Moor BD, Moreau Y (2012) Optimized data fusion for kernel k-means clustering. IEEE Trans Pattern Anal Mach Intell 34(5):1031\u20131039","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"5616_CR22","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1109\/TNNLS.2019.2919900","volume":"31","author":"S Zhou","year":"2020","unstructured":"Zhou S, Liu X, Li M, Zhu E, Liu L, Zhang C, Yin J (2020) Multiple kernel clustering with neighbor-kernel subspace segmentation. IEEE Trans Neural Netw Learn Syst 31(4):1351\u20131362","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"5616_CR23","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1109\/TPAMI.2019.2895608","volume":"42","author":"X Liu","year":"2020","unstructured":"Liu X, Wang L, Zhu X, Li M, Zhu E, Liu T, Liu L, Dou Y, Yin J (2020) Absent multiple kernel learning algorithms. IEEE Trans Pattern Anal Mach Intell 42(6):1303\u20131316","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5616_CR24","doi-asserted-by":"crossref","unstructured":"Liu X (2022) Simplemkkm: simple multiple kernel k-means. IEEE Trans Pattern Anal Mach Intell (01):1\u201313","DOI":"10.1109\/TPAMI.2022.3233635"},{"key":"5616_CR25","doi-asserted-by":"crossref","unstructured":"Gao H, Nie F, Li X, Huang H (2016) Multi-view subspace clustering. In: Proceedings of IEEE international conference on computer vision, pp 4234\u20134246","DOI":"10.1109\/ICCV.2015.482"},{"key":"5616_CR26","doi-asserted-by":"crossref","unstructured":"Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 4333\u20134341","DOI":"10.1109\/CVPR.2017.461"},{"key":"5616_CR27","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1609\/aaai.v34i04.5756","volume":"34","author":"MS Chen","year":"2020","unstructured":"Chen MS, Huang L, Wang CD, Huang D (2020) Multi-view clustering in latent embedding space. Proceedings of the AAAI conference on artificial intelligence 34:3513\u20133520","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"issue":"1","key":"5616_CR28","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/TCSVT.2021.3055625","volume":"32","author":"Y Chen","year":"2022","unstructured":"Chen Y, Xiao X, Peng C, Lu G, Zhou Y (2022) Low-rank tensor graph learning for multi-view subspace clustering. IEEE Trans Circ Syst Vid Technol 32(1):92\u2013104","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"issue":"12","key":"5616_CR29","doi-asserted-by":"publisher","first-page":"4784","DOI":"10.1109\/TCSVT.2021.3055039","volume":"31","author":"Y Jia","year":"2021","unstructured":"Jia Y, Liu H, Hou J, Kwong S, Zhang Q (2021) Multi-view spectral clustering tailored tensor low-rank representation. IEEE Trans Circ Syst Vid Technol 31(12):4784\u20134797","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"issue":"6","key":"5616_CR30","doi-asserted-by":"publisher","first-page":"3561","DOI":"10.1109\/TCSVT.2021.3119956","volume":"32","author":"M Lan","year":"2022","unstructured":"Lan M, Meng M, Yu J, Wu J (2022) Generalized multi-view collaborative subspace clustering. IEEE Trans Circ Syst Vid Technol 32(6):3561\u20133574","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"key":"5616_CR31","unstructured":"Kumar A, Rai P, Daum\u00e9 H (2011) Co-regularized multi-view spectral clustering. In: Proceedings of the international conference on neural information processing systems, pp 1413\u20131421"},{"key":"5616_CR32","unstructured":"Nie F, Jing L, Li X (2016) Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: Proceedings of the international joint conference on artificial intelligence, pp 1881\u20131887"},{"key":"5616_CR33","doi-asserted-by":"crossref","unstructured":"Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Proceedings of the AAAI conference on artificial intelligence, pp 2408\u20132414","DOI":"10.1609\/aaai.v31i1.10909"},{"key":"5616_CR34","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.knosys.2018.10.022","volume":"163","author":"W Hao","year":"2019","unstructured":"Hao W, Yan YA, Bing LB, Hf C (2019) A study of graph-based system for multi-view clustering. Knowl-Based Syst 163:1009\u20131019","journal-title":"Knowl-Based Syst"},{"issue":"10","key":"5616_CR35","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1109\/TCYB.2017.2751646","volume":"48","author":"K Zhan","year":"2018","unstructured":"Zhan K, Zhang C, Guan J, Wang J (2018) Graph learning for multiview clustering. IEEE Trans Cybern 48(10):2887\u20132895","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"5616_CR36","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/TIP.2018.2877335","volume":"28","author":"K Zhan","year":"2019","unstructured":"Zhan K, Nie F, Wang J, Yang Y (2019) Multiview consensus graph clustering. IEEE Trans Image Process 28(3):1261\u20131270","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"5616_CR37","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 (2020) GMC: graph-based multi-view clustering. IEEE Trans Knowl Data Eng 32(6):1116\u20131129","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"5616_CR38","doi-asserted-by":"publisher","first-page":"1984","DOI":"10.1109\/TKDE.2018.2872061","volume":"31","author":"K Zhan","year":"2019","unstructured":"Zhan K, Niu C, Chen C, Nie F, Zhang C, Yang Y (2019) Graph structure fusion for multiview clustering. IEEE Trans Knowl Data Eng 31(10):1984\u20131993","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5616_CR39","doi-asserted-by":"crossref","unstructured":"Xia R, Pan Y, Du L, Yin J (2014) Robust multi-view spectral clustering via low-rank and sparse decomposition. In: Proceedings of the AAAI conference on artificial intelligence, pp 2149\u20132155","DOI":"10.1609\/aaai.v28i1.8950"},{"issue":"1","key":"5616_CR40","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TKDE.2023.3283425","volume":"36","author":"Z Yu","year":"2024","unstructured":"Yu Z, Zhong Z, Yang K, Cao W, Chen CLP (2024) Broad learning autoencoder with graph structure for data clustering. IEEE Trans Knowl Data Eng 36(1):49\u201361","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"5616_CR41","doi-asserted-by":"publisher","first-page":"12369","DOI":"10.1109\/TKDE.2023.3271120","volume":"35","author":"Y Shi","year":"2023","unstructured":"Shi Y, Yang K, Yu Z, Chen CLP, Zeng H (2023) Adaptive ensemble clustering with boosting bls-based autoencoder. IEEE Trans Knowl Data Eng 35(12):12369\u201312383","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"5616_CR42","first-page":"2750","volume":"35","author":"S Yang","year":"2023","unstructured":"Yang S, Yu K, Cao F, Liu L, Wang H, Li J (2023) Learning causal representations for robust domain adaptation. IEEE Trans Knowl Data Eng 35(3):2750\u20132764","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5616_CR43","doi-asserted-by":"crossref","unstructured":"Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, pp 977\u2013986","DOI":"10.1145\/2623330.2623726"},{"key":"5616_CR44","doi-asserted-by":"crossref","unstructured":"Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):220\u2013227","DOI":"10.1109\/TPAMI.2008.79"},{"key":"5616_CR45","doi-asserted-by":"crossref","unstructured":"Fan K (1949) On a theorem of weyl concerning eigenvalues of linear transformations I. Proceedings of the National Academy of Sciences of the United States of America 35(11):652\u2013655","DOI":"10.1073\/pnas.35.11.652"},{"key":"5616_CR46","unstructured":"Gu S, Zhang L, Zuo W, Feng X (2014) Proceedings of projective dictionary pair learning for pattern classification. In: Advances in neural information processing systems pp 793\u2013801"},{"key":"5616_CR47","doi-asserted-by":"crossref","unstructured":"Bartels RH, Stewart GW (1972) Solution of the matrix equation AX + XB = C. Communications of the ACM 15(9):820\u2013826","DOI":"10.1145\/361573.361582"},{"key":"5616_CR48","unstructured":"Huang S, Tsang I, Xu Z, Lv JC (2021) Measuring diversity in graph learning: a unified framework for structured multi-view clustering. IEEE Trans Knowledge Data Eng 14(8):1\u201314"},{"key":"5616_CR49","doi-asserted-by":"crossref","unstructured":"Ikizler N, Cinbis RG, Pehlivan S, Duygulu P (2008) Recognizing actions from still images. In: Proceedings of the 19th international conference on pattern recognition 2008:1\u20134","DOI":"10.1109\/ICPR.2008.4761663"},{"key":"5616_CR50","doi-asserted-by":"crossref","unstructured":"Tang C, Liu X, Zhu X, Zhu E, Luo Z, Wang L, Gao W (2020b) CGD: multi-view clustering via cross-view graph diffusion pp 5924\u20135931","DOI":"10.1609\/aaai.v34i04.6052"},{"key":"5616_CR51","doi-asserted-by":"crossref","unstructured":"Huang S, Tsang IWH, Xu Z, Lv J, Liu QH (2022) Multi-view clustering on topological manifold. In: AAAI Conference on artificial intelligence 2022:7462\u20137469","DOI":"10.1609\/aaai.v36i6.20652"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05616-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05616-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05616-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T12:39:25Z","timestamp":1723034365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05616-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,29]]},"references-count":51,"journal-issue":{"issue":"17-18","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["5616"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05616-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,29]]},"assertion":[{"value":"13 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}