{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:47:50Z","timestamp":1762508870625,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:00:00Z","timestamp":1665187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:00:00Z","timestamp":1665187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076115"],"award-info":[{"award-number":["62076115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018617","name":"LiaoNing Revitalization Talents Program","doi-asserted-by":"crossref","award":["XLYC1907169"],"award-info":[{"award-number":["XLYC1907169"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Program of Star of Dalian Youth Science and Technology","award":["2019RQ033","2020RQ053"],"award-info":[{"award-number":["2019RQ033","2020RQ053"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["2018AAA0100300"],"award-info":[{"award-number":["2018AAA0100300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s00521-022-07864-4","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T10:04:47Z","timestamp":1665223487000},"page":"3203-3219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep multi-view subspace clustering via structure-preserved multi-scale features fusion"],"prefix":"10.1007","volume":"35","author":[{"given":"Kaiqiang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4846-2231","authenticated-orcid":false,"given":"Kewei","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixun","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"issue":"11","key":"7864_CR1","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","volume":"35","author":"E Elhamifar","year":"2013","unstructured":"Elhamifar E, Vidal R (2013) Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765\u20132781. https:\/\/doi.org\/10.1109\/TPAMI.2013.57","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"7864_CR2","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. https:\/\/doi.org\/10.1109\/TPAMI.2012.88","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7864_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33786-4_26","author":"C-Y Lu","year":"2012","unstructured":"Lu C-Y, Min H, Zhao Z-Q, Zhu L, Huang D-S, Yan S (2012) Robust and efficient subspace segmentation via least squares regression. Comput Vis ECCV. https:\/\/doi.org\/10.1007\/978-3-642-33786-4_26","journal-title":"Comput Vis ECCV"},{"key":"7864_CR4","doi-asserted-by":"publisher","unstructured":"Hu H, Lin Z, Feng J, Zhou J (2014) Smooth representation clustering. In: 2014 IEEE conference on computer vision and pattern recognition. pp 3834\u20133841. https:\/\/doi.org\/10.1109\/CVPR.2014.484","DOI":"10.1109\/CVPR.2014.484"},{"issue":"10","key":"7864_CR5","doi-asserted-by":"publisher","first-page":"5076","DOI":"10.1109\/TIP.2018.2848470","volume":"27","author":"X Peng","year":"2018","unstructured":"Peng X, Feng J, Xiao S, Yau W-Y, Zhou JT, Yang S (2018) Structured autoencoders for subspace clustering. IEEE Trans Image Process 27(10):5076\u20135086. https:\/\/doi.org\/10.1109\/TIP.2018.2848470","journal-title":"IEEE Trans Image Process"},{"key":"7864_CR6","doi-asserted-by":"publisher","unstructured":"Gao H, Nie F, Li X, Huang H (2015) Multi-view subspace clustering. In: 2015 IEEE international conference on computer vision (ICCV). pp 4238\u20134246. https:\/\/doi.org\/10.1109\/ICCV.2015.482","DOI":"10.1109\/ICCV.2015.482"},{"key":"7864_CR7","doi-asserted-by":"publisher","unstructured":"Cao X, Zhang C, Fu H, Liu S, Zhang H (2015) Diversity-induced multi-view subspace clustering. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). pp 586\u2013594. https:\/\/doi.org\/10.1109\/CVPR.2015.7298657","DOI":"10.1109\/CVPR.2015.7298657"},{"key":"7864_CR8","doi-asserted-by":"publisher","unstructured":"Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). pp 4333\u20134341. https:\/\/doi.org\/10.1109\/CVPR.2017.461","DOI":"10.1109\/CVPR.2017.461"},{"key":"7864_CR9","doi-asserted-by":"publisher","unstructured":"Wang X, Guo X, Lei Z, Zhang C, Li SZ (2017) Exclusivity-consistency regularized multi-view subspace clustering. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2017.8","DOI":"10.1109\/CVPR.2017.8"},{"key":"7864_CR10","doi-asserted-by":"crossref","unstructured":"Luo S, Zhang C, Zhang W, Cao X (2018) Consistent and specific multi-view subspace clustering. In: Thirty-second AAAI conference on artificial intelligence. pp 3730\u20133737","DOI":"10.1609\/aaai.v32i1.11617"},{"key":"7864_CR11","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.patcog.2019.05.005","volume":"93","author":"W Zhu","year":"2019","unstructured":"Zhu W, Lu J, Zhou J (2019) Structured general and specific multi-view subspace clustering. Pattern Recogn 93:392\u2013403. https:\/\/doi.org\/10.1016\/j.patcog.2019.05.005","journal-title":"Pattern Recogn"},{"key":"7864_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9","author":"M Caron","year":"2018","unstructured":"Caron M, Bojanowski P, Joulin A, Douze M (2018) Deep clustering for unsupervised learning of visual features. Comput Vis ECCV. https:\/\/doi.org\/10.1007\/978-3-030-01264-9_9","journal-title":"Comput Vis ECCV"},{"key":"7864_CR13","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International conference on machine learning. pp 478\u2013487"},{"issue":"2","key":"7864_CR14","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1007\/s00521-021-06581-8","volume":"34","author":"D Skiadopoulou","year":"2022","unstructured":"Skiadopoulou D, Likas A (2022) Face clustering using a weighted combination of deep representations. Neural Comput Appl 34(2):995\u20131006. https:\/\/doi.org\/10.1007\/s00521-021-06581-8","journal-title":"Neural Comput Appl"},{"issue":"6","key":"7864_CR15","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1109\/JSTSP.2018.2875385","volume":"12","author":"M Abavisani","year":"2018","unstructured":"Abavisani M, Patel VM (2018) Deep multimodal subspace clustering networks. IEEE J Sel Top Signal Process 12(6):1601\u20131614. https:\/\/doi.org\/10.1109\/JSTSP.2018.2875385","journal-title":"IEEE J Sel Top Signal Process"},{"issue":"1","key":"7864_CR16","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. https:\/\/doi.org\/10.1109\/TPAMI.2018.2877660","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7864_CR17","unstructured":"Zhu P, Hui B, Zhang C, Du D, Wen L, Hu Q (2019) Multi-view deep subspace clustering networks. ArXiv: 1908.01978"},{"key":"7864_CR18","doi-asserted-by":"publisher","unstructured":"Li R, Zhang C, Fu H, Peng X, Zhou JT, Hu Q (2019) Reciprocal multi-layer subspace learning for multi-view clustering. In: 2019 IEEE\/CVF international conference on computer vision (ICCV). pp 8171\u20138179. https:\/\/doi.org\/10.1109\/ICCV.2019.00826","DOI":"10.1109\/ICCV.2019.00826"},{"key":"7864_CR19","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.neucom.2019.10.074","volume":"379","author":"Q Zheng","year":"2020","unstructured":"Zheng Q, Zhu J, Li Z, Pang S, Wang J, Li Y (2020) Feature concatenation multi-view subspace clustering. Neurocomputing 379:89\u2013102. https:\/\/doi.org\/10.1016\/j.neucom.2019.10.074","journal-title":"Neurocomputing"},{"issue":"8","key":"7864_CR20","doi-asserted-by":"publisher","first-page":"3517","DOI":"10.1109\/TCYB.2019.2918495","volume":"50","author":"T Zhou","year":"2020","unstructured":"Zhou T, Zhang C, Peng X, Bhaskar H, Yang J (2020) Dual shared-specific multiview subspace clustering. IEEE Trans Cybern 50(8):3517\u20133530. https:\/\/doi.org\/10.1109\/TCYB.2019.2918495","journal-title":"IEEE Trans Cybern"},{"issue":"22","key":"7864_CR21","doi-asserted-by":"publisher","first-page":"15397","DOI":"10.1007\/s00521-021-06166-5","volume":"33","author":"X Liu","year":"2021","unstructured":"Liu X, Pan G, Xie M (2021) Multi-view subspace clustering with adaptive locally consistent graph regularization. Neural Comput Appl 33(22):15397\u201315412. https:\/\/doi.org\/10.1007\/s00521-021-06166-5","journal-title":"Neural Comput Appl"},{"key":"7864_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105514","volume":"194","author":"Q Zheng","year":"2020","unstructured":"Zheng Q, Zhu J, Tian Z, Li Z, Pang S, Jia X (2020) Constrained bilinear factorization multi-view subspace clustering. Knowl-Based Syst 194:105514. https:\/\/doi.org\/10.1016\/j.knosys.2020.105514","journal-title":"Knowl-Based Syst"},{"key":"7864_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108196","volume":"121","author":"X Si","year":"2022","unstructured":"Si X, Yin Q, Zhao X, Yao L (2022) Consistent and diverse multi-view subspace clustering with structure constraint. Pattern Recogn 121:108196. https:\/\/doi.org\/10.1016\/j.patcog.2021.108196","journal-title":"Pattern Recogn"},{"key":"7864_CR24","doi-asserted-by":"publisher","unstructured":"Gretton A, Bousquet O, Smola A, Sch\u00f6lkopf B (2005) Measuring statistical dependence with hilbert-schmidt norms. In: Algorithmic learning theory. pp 63\u201377. https:\/\/doi.org\/10.1007\/11564089_7","DOI":"10.1007\/11564089_7"},{"key":"7864_CR25","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neucom.2021.03.115","volume":"449","author":"Q Zheng","year":"2021","unstructured":"Zheng Q, Zhu J, Ma Y, Li Z, Tian Z (2021) Multi-view subspace clustering networks with local and global graph information. Neurocomputing 449:15\u201323. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.115","journal-title":"Neurocomputing"},{"key":"7864_CR26","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.neucom.2021.01.011","volume":"435","author":"R-K Lu","year":"2021","unstructured":"Lu R-K, Liu J-W, Zuo X (2021) Attentive multi-view deep subspace clustering net. Neurocomputing 435:186\u2013196. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.011","journal-title":"Neurocomputing"},{"key":"7864_CR27","doi-asserted-by":"publisher","unstructured":"Dang Z, Deng C, Yang X, Huang H (2020) Multi-scale fusion subspace clustering using similarity constraint. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). pp 6657\u20136666. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00669","DOI":"10.1109\/CVPR42600.2020.00669"},{"issue":"6","key":"7864_CR28","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373\u20131396. https:\/\/doi.org\/10.1162\/089976603321780317","journal-title":"Neural Comput"},{"key":"7864_CR29","unstructured":"Lin Z, Chen M, Ma Y (2010) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055"},{"key":"7864_CR30","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. vol. 28","DOI":"10.1609\/aaai.v28i1.8950"},{"key":"7864_CR31","doi-asserted-by":"publisher","first-page":"30691","DOI":"10.1109\/ACCESS.2018.2842078","volume":"6","author":"Q Kou","year":"2018","unstructured":"Kou Q, Cheng D, Chen L, Zhao K (2018) A multiresolution gray-scale and rotation invariant descriptor for texture classification. IEEE Access 6:30691\u201330701. https:\/\/doi.org\/10.1109\/ACCESS.2018.2842078","journal-title":"IEEE Access"},{"issue":"3","key":"7864_CR32","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1109\/12.210173","volume":"42","author":"M Lades","year":"1993","unstructured":"Lades M, Vorbruggen JC, Buhmann J, Lange J, von der Malsburg C, Wurtz RP, Konen W (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300\u2013311. https:\/\/doi.org\/10.1109\/12.210173","journal-title":"IEEE Trans Comput"},{"key":"7864_CR33","doi-asserted-by":"publisher","unstructured":"Xu J, Han J, Nie F (2016) Discriminatively embedded k-means for multi-view clustering. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). pp 5356\u20135364. https:\/\/doi.org\/10.1109\/CVPR.2016.578","DOI":"10.1109\/CVPR.2016.578"},{"issue":"5786","key":"7864_CR34","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science"},{"issue":"86","key":"7864_CR35","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(86):2579\u20132605","journal-title":"J Mach Learn Res"},{"issue":"6","key":"7864_CR36","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. https:\/\/doi.org\/10.1109\/TKDE.2019.2903810","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7864_CR37","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neunet.2019.10.010","volume":"122","author":"Z Kang","year":"2020","unstructured":"Kang Z, Zhao X, Peng C, Zhu H, Zhou JT, Peng X, Chen W, Xu Z (2020) Partition level multiview subspace clustering. Neural Netw 122:279\u2013288. https:\/\/doi.org\/10.1016\/j.neunet.2019.10.010","journal-title":"Neural Netw"},{"key":"7864_CR38","doi-asserted-by":"publisher","first-page":"4412","DOI":"10.1609\/aaai.v34i04.5867","volume":"34","author":"Z Kang","year":"2020","unstructured":"Kang Z, Zhou W, Zhao Z, Shao J, Han M, Xu Z (2020) Large-scale multi-view subspace clustering in linear time. Proc AAAI Conf Artif Intell 34:4412\u20134419. https:\/\/doi.org\/10.1609\/aaai.v34i04.5867","journal-title":"Proc AAAI Conf Artif Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07864-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07864-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07864-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T05:03:42Z","timestamp":1674795822000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07864-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,8]]},"references-count":38,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["7864"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07864-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,10,8]]},"assertion":[{"value":"12 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}