{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T05:27:07Z","timestamp":1747459627162,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"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":["61772020"],"award-info":[{"award-number":["61772020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["Grant No. 2024JC-YBQN-0690"],"award-info":[{"award-number":["Grant No. 2024JC-YBQN-0690"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["Grant No. 23JK0665"],"award-info":[{"award-number":["Grant No. 23JK0665"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10489-024-05812-4","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T05:01:56Z","timestamp":1726203716000},"page":"12378-12390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Affinity adaptive sparse subspace clustering via constrained Laplacian rank"],"prefix":"10.1007","volume":"54","author":[{"given":"Ting","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4764-9483","authenticated-orcid":false,"given":"Shuisheng","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Zhuan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"issue":"1","key":"5812_CR1","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/s10489-021-02409-z","volume":"52","author":"SE Abhadiomhen","year":"2022","unstructured":"Abhadiomhen SE, Wang Z, Shen X (2022) Coupled low rank representation and subspace clustering. Appl Intell 52(1):530\u201346","journal-title":"Appl Intell"},{"key":"5812_CR2","doi-asserted-by":"publisher","first-page":"13987","DOI":"10.1007\/s10489-021-02974-3","volume":"52","author":"L Luo","year":"2022","unstructured":"Luo L, Liang Q, Zhang X-Q, Xue X-Q, Liu Z-G (2022) Joint learning affinity matrix and representation matrix for robust low-rank multi-kernel clustering. Appl Intell 52:13987\u201314004","journal-title":"Appl Intell"},{"doi-asserted-by":"crossref","unstructured":"Zhang G-Y, Chen X-W, Zhou YR, Wang C-D, Huang D, He X-Y (2022) Kernelized multi-view subspace clustering via auto-weighted graph learning. Applied Intelligence 52:716\u2013731","key":"5812_CR3","DOI":"10.1007\/s10489-021-02365-8"},{"key":"5812_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2022.108622","volume":"199","author":"T Yang","year":"2022","unstructured":"Yang T, Zhou S, Zhang Z (2022) The k-sparse lsr for subspace clustering via 0\u20131 integer programming. Signal Process 199:108622","journal-title":"Signal Process"},{"key":"5812_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108082","volume":"185","author":"Y Guo","year":"2021","unstructured":"Guo Y, Tierney S, Gao J (2021) Efficient sparse subspace clustering by nearest neighbour filtering. Signal Process 185:108082","journal-title":"Signal Process"},{"issue":"24","key":"5812_CR6","doi-asserted-by":"publisher","first-page":"6557","DOI":"10.1109\/TSP.2016.2613070","volume":"64","author":"CG Li","year":"2016","unstructured":"Li CG, Vidal R (2016) A structured sparse plus structured low-rank framework for subspace clustering and completion. IEEE Trans Signal Process 64(24):6557\u20136570","journal-title":"IEEE Trans Signal Process"},{"doi-asserted-by":"crossref","unstructured":"Bako L, Vidal R (2008) Algebraic identification of mimo sarx models. In: Hybrid Systems: Computation and Control: 11th International Workshop, HSCC 2008, St. Louis, MO, USA, April 22-24, 2008. Proceedings 11, pp 43\u201357. Springer","key":"5812_CR7","DOI":"10.1007\/978-3-540-78929-1_4"},{"key":"5812_CR8","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1007\/s10618-013-0317-y","volume":"28","author":"B McWilliams","year":"2014","unstructured":"McWilliams B, Montana G (2014) Subspace clustering of high-dimensional data: a predictive approach. Data Min Knowl Disc 28:736\u2013772","journal-title":"Data Min Knowl Disc"},{"issue":"2","key":"5812_CR9","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MSP.2010.939739","volume":"28","author":"R Vidal","year":"2011","unstructured":"Vidal R (2011) Subspace clustering. IEEE Signal Process Mag 28(2):52\u201368","journal-title":"IEEE Signal Process Mag"},{"issue":"11","key":"5812_CR10","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","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"5812_CR11","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","volume":"35","author":"G Liu","year":"2012","unstructured":"Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2012) 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"},{"doi-asserted-by":"crossref","unstructured":"Lu CY, Min H, Zhao ZQ, Zhu L, Huang DS, Yan S (2012) Robust and efficient subspace segmentation via least squares regression. In: Computer Vision\u2013ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VII 12, pp 347\u2013360. Springer","key":"5812_CR12","DOI":"10.1007\/978-3-642-33786-4_26"},{"issue":"2","key":"5812_CR13","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/TPAMI.2018.2794348","volume":"41","author":"C Lu","year":"2018","unstructured":"Lu C, Feng J, Lin Z, Mei T, Yan S (2018) Subspace clustering by block diagonal representation. IEEE Trans Pattern Anal Mach Intell 41(2):487\u2013501","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"doi-asserted-by":"crossref","unstructured":"Wen J, Fang X, Xu Y, Tian C, Fei L (2018) Low-rank representation with adaptive graph regularization. Neural Netw 108:83\u201396","key":"5812_CR14","DOI":"10.1016\/j.neunet.2018.08.007"},{"unstructured":"Ng A, Jordan M, Weiss Y (2001) On spectral clustering: analysis and an algorithm. Advances in Neural Information Processing Systems 14","key":"5812_CR15"},{"doi-asserted-by":"crossref","unstructured":"Liu Y, Yang X, Zhou S, Liu X, Wang S, Liang K, Tu W, Li L (2023) Simple contrastive graph clustering. IEEE Transactions on Neural Networks and Learning Systems","key":"5812_CR16","DOI":"10.1109\/TNNLS.2023.3271871"},{"unstructured":"Liu Y, Liang K, Xia J, Zhou S, Yang X, Liu X, Li SZ (2023) Dink-net: Neural clustering on large graphs. In: International Conference on Machine Learning, pp 21794\u201321812. PMLR","key":"5812_CR17"},{"doi-asserted-by":"crossref","unstructured":"Cai J, Zhang Y, Wang S, Fan J, Guo W (2024) Wasserstein embedding learning for deep clustering: a generative approach. IEEE Transactions on Multimedia","key":"5812_CR18","DOI":"10.1109\/TMM.2024.3369862"},{"key":"5812_CR19","doi-asserted-by":"publisher","first-page":"108386","DOI":"10.1016\/j.patcog.2021.108386","volume":"123","author":"J Cai","year":"2022","unstructured":"Cai J, Wang S, Xu C, Guo W (2022) Unsupervised deep clustering via contractive feature representation and focal loss. Pattern Recogn 123:108386","journal-title":"Pattern Recogn"},{"doi-asserted-by":"crossref","unstructured":"Cai J, Fan J, Guo W, Wang S, Zhang Y, Zhang Z (2022) Efficient deep embedded subspace clustering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1\u201310","key":"5812_CR20","DOI":"10.1109\/CVPR52688.2022.00012"},{"doi-asserted-by":"crossref","unstructured":"Nie F, Wang X, Jordan M, Huang H (2016) The constrained laplacian rank algorithm for graph-based clustering. In: Proceedings of the AAAI conference on artificial intelligence, volume\u00a030","key":"5812_CR21","DOI":"10.1609\/aaai.v30i1.10302"},{"key":"5812_CR22","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Von Luxburg","year":"2007","unstructured":"Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17:395\u2013416","journal-title":"Stat Comput"},{"issue":"8","key":"5812_CR23","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888\u2013905","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"5812_CR24","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67(2):301\u2013320","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"issue":"871\u2013898","key":"5812_CR25","first-page":"12","volume":"2","author":"B Mohar","year":"1991","unstructured":"Mohar B, Alavi Y, Chartrand G, Oellermann O (1991) The laplacian spectrum of graphs. Graph Theory, Combinatorics, and Applications 2(871\u2013898):12","journal-title":"Graph Theory, Combinatorics, and Applications"},{"doi-asserted-by":"crossref","unstructured":"Chung FRK (1997) Spectral graph theory, vol\u00a092. American Mathematical Soc","key":"5812_CR26","DOI":"10.1090\/cbms\/092"},{"issue":"11","key":"5812_CR27","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1073\/pnas.35.11.652","volume":"35","author":"K Fan","year":"1949","unstructured":"Fan K (1949) On a theorem of weyl concerning eigenvalues of linear transformations. Proc Natl Acad Sci 35(11):652\u2013655","journal-title":"Proc Natl Acad Sci"},{"doi-asserted-by":"crossref","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J et\u00a0al (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends\u00ae in Machine learning 3(1):1\u2013122","key":"5812_CR28","DOI":"10.1561\/2200000016"},{"unstructured":"Huang J, Nie F, Huang H (2015) A new simplex sparse learning model to measure data similarity for clustering. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI\u201915, pp 3569\u20133575. AAAI Press","key":"5812_CR29"},{"key":"5812_CR30","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.neunet.2018.10.001","volume":"109","author":"S Zhan","year":"2019","unstructured":"Zhan S, Wu J, Han N, Wen J, Fang X (2019) Unsupervised feature extraction by low-rank and sparsity preserving embedding. Neural Netw 109:56\u201366","journal-title":"Neural Netw"},{"issue":"4","key":"5812_CR31","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1109\/TCYB.2018.2799862","volume":"49","author":"J Wen","year":"2018","unstructured":"Wen J, Han N, Fang X, Fei L, Yan K, Zhan S (2018) Low-rank preserving projection via graph regularized reconstruction. IEEE Transactions on Cybernetics 49(4):1279\u20131291","journal-title":"IEEE Transactions on Cybernetics"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05812-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05812-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05812-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T13:11:22Z","timestamp":1727701882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05812-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":31,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["5812"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05812-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,9,13]]},"assertion":[{"value":"22 August 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2024","order":2,"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\/Competing Interests"}},{"value":"The data used in this study are available from the following resources in the public domain:","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and Informed Consent for Data Used"}}]}}