{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:22:11Z","timestamp":1758273731220,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"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":["Appl Intell"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10489-021-02974-3","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T17:03:13Z","timestamp":1646154193000},"page":"13987-14004","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Joint learning affinity matrix and representation matrix for robust low-rank multi-kernel clustering"],"prefix":"10.1007","volume":"52","author":[{"given":"Liang","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4007-4501","authenticated-orcid":false,"given":"Xiaoqian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuqian","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"2974_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ins.2018.02.052","volume":"450","author":"B Chen","year":"2018","unstructured":"Chen B, Sun H, Xia G, Feng L, Li B (2018) Human motion recovery utilizing truncated schatten p-norm and kinematic constraints. Inf Sci 450:89\u2013108","journal-title":"Inf Sci"},{"key":"2974_CR2","doi-asserted-by":"crossref","unstructured":"Chen J, Yang S, Mao H, Fahy C (2021) Multiview subspace clustering using low-rank representation. IEEE Transactions on Cybernetics","DOI":"10.1109\/TCYB.2021.3087114"},{"key":"2974_CR3","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/RBME.2018.2798701","volume":"11","author":"X Chen","year":"2018","unstructured":"Chen X, Pan L (2018) A survey of graph cuts\/graph search based medical image segmentation. IEEE Rev Biomed Eng 11:112\u2013124. https:\/\/doi.org\/10.1109\/RBME.2018.2798701","journal-title":"IEEE Rev Biomed Eng"},{"issue":"1","key":"2974_CR4","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.patcog.2011.06.004","volume":"45","author":"X Chen","year":"2012","unstructured":"Chen X, Ye Y, Xu X, Huang JZ (2012) A feature group weighting method for subspace clustering of high-dimensional data. Pattern Recogn 45(1):434\u2013446","journal-title":"Pattern Recogn"},{"key":"2974_CR5","unstructured":"Dattorro J (2010) Convex optimization & Euclidean distance geometry. Lulu Com"},{"issue":"1","key":"2974_CR6","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/TPAMI.2008.277","volume":"32","author":"C Ding","year":"2008","unstructured":"Ding C, Li T, Jordan MI (2008) Convex and semi-nonnegative matrix factorizations. IEEE Trans Pattern Anal Mach Intell 32(1):45\u201355","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2974_CR7","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.ins.2017.11.016","volume":"429","author":"S Ding","year":"2018","unstructured":"Ding S, Jia H, Du M, Xue Y (2018) A semi-supervised approximate spectral clustering algorithm based on hmrf model. Inf Sci 429:215\u2013228","journal-title":"Inf Sci"},{"key":"2974_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2017.08.043","volume":"420","author":"Y Djenouri","year":"2017","unstructured":"Djenouri Y, Comuzzi M (2017) Combining apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem. Inf Sci 420:1\u201315","journal-title":"Inf Sci"},{"key":"2974_CR9","unstructured":"Du L, Zhou P, Shi L, Wang H, Fan M, Wang W, Shen YD (2015) Robust multiple kernel k-means using l21-norm. In: Twenty-fourth international joint conference on artificial intelligence"},{"key":"2974_CR10","doi-asserted-by":"crossref","unstructured":"Elhamifar E, Vidal R (2009) Sparse subspace clustering. In: IEEE Conference on computer vision & pattern recognition","DOI":"10.1109\/CVPR.2009.5206547"},{"issue":"11","key":"2974_CR11","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"},{"key":"2974_CR12","first-page":"636","volume":"50","author":"R Ghaemi","year":"2009","unstructured":"Ghaemi R, Sulaiman MN, Ibrahim H, Mustapha N, et al. (2009) A survey: clustering ensembles techniques. World Academy of Science. Eng Technol 50:636\u2013645","journal-title":"Eng Technol"},{"issue":"2","key":"2974_CR13","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s11263-016-0930-5","volume":"121","author":"S Gu","year":"2017","unstructured":"Gu S, Xie Q, Meng D, Zuo W, Feng X, Zhang L (2017) Weighted nuclear norm minimization and its applications to low level vision. Int J Comput Vis 121(2):183\u2013208","journal-title":"Int J Comput Vis"},{"key":"2974_CR14","unstructured":"Guo X (2015) Robust subspace segmentation by simultaneously learning data representations and their affinity matrix. In: Twenty-fourth international joint conference on artificial intelligence"},{"key":"2974_CR15","unstructured":"Ho J, Yang MH, Lim J, Lee KC, Kriegman D (2003) Clustering appearances of objects under varying illumination conditions. In: 2003 IEEE Computer society conference on computer vision and pattern recognition, 2003. Proceedings. IEEE, vol 1, pp i\u2013i"},{"issue":"1","key":"2974_CR16","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/TFUZZ.2011.2170175","volume":"20","author":"H Huang","year":"2011","unstructured":"Huang H, Chuang YY, Chen CS (2011) Multiple kernel fuzzy clustering. IEEE Trans Fuzzy Syst 20(1):120\u2013134","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"2974_CR17","unstructured":"Huang H, Chuang YY, Chen CS (2012) Affinity aggregation for spectral clustering. In: 2012 IEEE Conference on computer vision and pattern recognition, pp 773\u2013780. IEEE"},{"key":"2974_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2018.05.017","volume":"158","author":"S Huang","year":"2018","unstructured":"Huang S, Kang Z, Xu Z (2018) Self-weighted multi-view clustering with soft capped norm. Knowl-Based Syst 158:1\u20138","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"2974_CR19","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s10618-017-0543-9","volume":"32","author":"S Huang","year":"2018","unstructured":"Huang S, Wang H, Li T, Li T, Xu Z (2018) Robust graph regularized nonnegative matrix factorization for clustering. Data Min Knowl Disc 32(2):483\u2013503","journal-title":"Data Min Knowl Disc"},{"key":"2974_CR20","doi-asserted-by":"crossref","unstructured":"Kang Z, Lu X, Yi J, Xu Z (2018) Self-weighted multiple kernel learning for graph-based clustering and semi-supervised classification. arXiv:1806.07697","DOI":"10.24963\/ijcai.2018\/320"},{"key":"2974_CR21","doi-asserted-by":"crossref","unstructured":"Kang Z, Peng C, Cheng Q (2017) Twin learning for similarity and clustering: a unified kernel approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31","DOI":"10.1609\/aaai.v31i1.10853"},{"key":"2974_CR22","doi-asserted-by":"crossref","unstructured":"Kang Z, Peng C, Cheng Q, Xu Z (2018) Unified spectral clustering with optimal graph. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11613"},{"key":"2974_CR23","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.knosys.2018.09.009","volume":"163","author":"Z Kang","year":"2019","unstructured":"Kang Z, Wen L, Chen W, Xu Z (2019) Low-rank kernel learning for graph-based clustering. Knowl-Based Syst 163:510\u2013 517","journal-title":"Knowl-Based Syst"},{"key":"2974_CR24","doi-asserted-by":"crossref","unstructured":"Lai H, Pan Y, Lu C, Tang Y, Yan S (2014) Efficient k-support matrix pursuit. In: European conference on computer vision. Springer, pp 617\u2013631","DOI":"10.1007\/978-3-319-10605-2_40"},{"key":"2974_CR25","doi-asserted-by":"crossref","unstructured":"Lewis DP, Jebara T, Noble WS (2006) Nonstationary kernel combination. In: Proceedings of the 23rd international conference on Machine learning, pp 553\u2013560","DOI":"10.1145\/1143844.1143914"},{"key":"2974_CR26","unstructured":"Li CG, Vidal R (2015) Structured sparse subspace clustering: a unified optimization framework. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 277\u2013286"},{"key":"2974_CR27","doi-asserted-by":"publisher","unstructured":"Li Y, Zhao Q, Luo K (2021) Multi-objective soft subspace clustering in the composite kernel space. Inf Sci 563:23\u201339. https:\/\/doi.org\/10.1016\/j.ins.2021.02.008. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025521001420","DOI":"10.1016\/j.ins.2021.02.008"},{"key":"2974_CR28","doi-asserted-by":"crossref","unstructured":"Liang Q, Zhang X, Luo L (2020) Robust multiple kernel subspace clustering based on low rank consensus kernel learning. In: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering, pp 621\u2013626","DOI":"10.1145\/3443467.3443824"},{"issue":"1","key":"2974_CR29","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"},{"key":"2974_CR30","unstructured":"Liu G, Lin Z, Yu Y, et al. (2010) Robust subspace segmentation by low-rank representation. In: Icml, vol 1. Citeseer, pp 8"},{"key":"2974_CR31","doi-asserted-by":"crossref","unstructured":"Liu M, Wang Y, Sun J, Ji Z (2020) Structured block diagonal representation for subspace clustering. Appl Intell:1\u201314","DOI":"10.1007\/s10489-020-01629-z"},{"key":"2974_CR32","doi-asserted-by":"crossref","unstructured":"Liu M, Wang Y, Sun J, Ji Z (2021) Adaptive low-rank kernel block diagonal representation subspace clustering. Appl Intell:1\u201316","DOI":"10.1007\/s10489-021-02396-1"},{"issue":"2","key":"2974_CR33","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"},{"key":"2974_CR34","doi-asserted-by":"crossref","unstructured":"Lu C, Feng J, Lin Z, Yan S (2013) Correlation adaptive subspace segmentation by trace lasso. In: Proceedings of the IEEE international conference on computer vision, pp 1345\u20131352","DOI":"10.1109\/ICCV.2013.170"},{"key":"2974_CR35","doi-asserted-by":"crossref","unstructured":"Lu C, Tang J, Lin M, Lin L, Yan S, Lin Z (2013) Correntropy induced l2 graph for robust subspace clustering. In: Proceedings of the IEEE international conference on computer vision, pp 1801\u20131808","DOI":"10.1109\/ICCV.2013.226"},{"key":"2974_CR36","doi-asserted-by":"crossref","unstructured":"Mi Y, Ren Z, Mukherjee M, Huang Y, Sun Q, Chen L (2021) Diversity and consistency embedding learning for multi-view subspace clustering. Appl Intell:1\u201314","DOI":"10.1007\/s10489-020-02126-z"},{"key":"2974_CR37","doi-asserted-by":"crossref","unstructured":"Nie F, Huang H, Ding C (2012) Low-rank matrix recovery via efficient schatten p-norm minimization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 26","DOI":"10.1609\/aaai.v26i1.8210"},{"key":"2974_CR38","doi-asserted-by":"crossref","unstructured":"Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 977\u2013986","DOI":"10.1145\/2623330.2623726"},{"key":"2974_CR39","doi-asserted-by":"publisher","first-page":"105040","DOI":"10.1016\/j.knosys.2019.105040","volume":"188","author":"Z Ren","year":"2020","unstructured":"Ren Z, Li H, Yang C, Sun Q (2020) Multiple kernel subspace clustering with local structural graph and low-rank consensus kernel learning. Knowl-Based Syst 188:105040","journal-title":"Knowl-Based Syst"},{"key":"2974_CR40","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.patcog.2018.02.008","volume":"79","author":"X Shi","year":"2018","unstructured":"Shi X, Guo Z, Xing F, Cai J, Yang L (2018) Self-learning for face clustering. Pattern Recogn 79:279\u2013289","journal-title":"Pattern Recogn"},{"key":"2974_CR41","doi-asserted-by":"crossref","unstructured":"Wang S, Yuan X, Yao T, Yan S, Shen J (2011) Efficient subspace segmentation via quadratic programming. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 25","DOI":"10.1609\/aaai.v25i1.7892"},{"issue":"1","key":"2974_CR42","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TPAMI.2017.2662005","volume":"40","author":"W Wang","year":"2017","unstructured":"Wang W, Shen J, Yang R, Porikli F (2017) Saliency-aware video object segmentation. IEEE Trans Pattern Anal Mach Intell 40(1):20\u201333","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2974_CR43","unstructured":"Wang YX, Xu H, Leng C (2013) Provable subspace clustering: When lrr meets ssc. In: NIPS, vol 1, pp 5"},{"issue":"10","key":"2974_CR44","doi-asserted-by":"publisher","first-page":"4842","DOI":"10.1109\/TIP.2016.2599290","volume":"25","author":"Y Xie","year":"2016","unstructured":"Xie Y, Gu S, Liu Y, Zuo W, Zhang W, Zhang L (2016) Weighted schatten p-norm minimization for image denoising and background subtraction. IEEE Trans Image Process 25(10):4842\u20134857","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"2974_CR45","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1109\/TPAMI.2007.70739","volume":"30","author":"J Yan","year":"2008","unstructured":"Yan J, Pollefeys M (2008) A factorization-based approach for articulated nonrigid shape, motion and kinematic chain recovery from video. IEEE Trans Pattern Anal Mach Intell 30(5):865\u2013877","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2974_CR46","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.ins.2019.05.063","volume":"500","author":"C Yang","year":"2019","unstructured":"Yang C, Ren Z, Sun Q, Wu M, Yin M, Sun Y (2019) Joint correntropy metric weighting and block diagonal regularizer for robust multiple kernel subspace clustering. Inf Sci 500:48\u201366","journal-title":"Inf Sci"},{"key":"2974_CR47","doi-asserted-by":"crossref","unstructured":"Zhang T, Tang Z, Liu Q (2017) Robust subspace clustering via joint weighted schatten-p norm and lq norm minimization. J Electron Imaging 26(3):033021","DOI":"10.1117\/1.JEI.26.3.033021"},{"key":"2974_CR48","doi-asserted-by":"publisher","unstructured":"Zhou S, Ou Q, Liu X, Wang S, Liu L, Wang S, Zhu E, Yin J, Xu X (2021) Multiple kernel clustering with compressed subspace alignment. IEEE Trans Neural Netw Learn Syst:1\u201312. https:\/\/doi.org\/10.1109\/TNNLS.2021.3093426","DOI":"10.1109\/TNNLS.2021.3093426"},{"key":"2974_CR49","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.ins.2017.07.019","volume":"417","author":"R Zhu","year":"2017","unstructured":"Zhu R, Xue JH (2017) On the orthogonal distance to class subspaces for high-dimensional data classification. Inf Sci 417:262\u2013273","journal-title":"Inf Sci"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02974-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02974-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02974-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T09:26:52Z","timestamp":1664616412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02974-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":49,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["2974"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02974-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,3,1]]},"assertion":[{"value":"14 October 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2022","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}