{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:50Z","timestamp":1740122810309,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Zhejiang Natural Science Foundation","doi-asserted-by":"crossref","award":["LY19F030006"],"award-info":[{"award-number":["LY19F030006"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Wenzhou major scientific and technological innovation project","award":["zy2019019"],"award-info":[{"award-number":["zy2019019"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s11063-021-10603-w","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T21:02:45Z","timestamp":1628542965000},"page":"4377-4388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Subspace Clustering via Integrating Sparse Representation and Adaptive Graph Learning"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2947-4018","authenticated-orcid":false,"given":"Zhiyang","family":"Gu","sequence":"first","affiliation":[]},{"given":"Zhenghong","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Yijie","family":"Huang","sequence":"additional","affiliation":[]},{"given":"De","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"key":"10603_CR1","unstructured":"Asuncion A, Newman D (2007) Uci machine learning repository https:\/\/archive-beta.ics.uci.edu\/ml\/datasets"},{"key":"10603_CR2","unstructured":"Cand\u00e8s EJ, Li X, Ma Y, Wright J (2009) Robust principal component analysis? CoRR, arXiv:0912.3599"},{"key":"10603_CR3","doi-asserted-by":"crossref","unstructured":"Cheng B, Liu G, Wang J, Yan S (2011) Multi-task low-rank affinity pursuit for image segmentation. In: International conference on computer vision, pp 2439\u20132446","DOI":"10.1109\/ICCV.2011.6126528"},{"issue":"3","key":"10603_CR4","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1023\/A:1008000628999","volume":"29","author":"C Jo","year":"1998","unstructured":"Jo C, Paulo O, Takeo K (1998) A multibody factorization method for independently moving objects. Int J Comput Vis 29(3):159\u2013179","journal-title":"Int J Comput Vis"},{"key":"10603_CR5","unstructured":"Dong W, Wu X (2018) Robust affine subspace clustering via smoothed $$\\ell _0$$-norm. Neural Processing Letters, pp 1\u201313"},{"issue":"11","key":"10603_CR6","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":"6","key":"10603_CR7","first-page":"726","volume":"24","author":"A Fischler Martin","year":"1981","unstructured":"Fischler Martin A, Bolles Robert C (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):726\u2013740","journal-title":"Commun ACM"},{"key":"10603_CR8","doi-asserted-by":"crossref","unstructured":"Goh A, Vidal R (2007) Segmenting motions of different types by unsupervised manifold clustering. In: IEEE conference on computer vision and pattern recognition, pp 1\u20136","DOI":"10.1109\/CVPR.2007.383235"},{"key":"10603_CR9","doi-asserted-by":"crossref","unstructured":"Ho J, Yang MH, Lim J, Lee KC (2003) Clustering appearances of objects under varying illumination conditions. In: 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. Proceedings, vol 1, pp I-11\u2013I-18","DOI":"10.1109\/CVPR.2003.1211332"},{"issue":"1","key":"10603_CR10","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1007\/s11063-020-10274-z","volume":"52","author":"W-B Hu","year":"2020","unstructured":"Hu W-B, Wu X-J (2020) Multi-geometric sparse subspace clustering. Neural Process Lett 52(1):849\u2013867","journal-title":"Neural Process Lett"},{"key":"10603_CR11","unstructured":"Ji P, Salzmann M, Li H (2014) Efficient dense subspace clustering. In: Applications of computer vision, pp 461\u2013468"},{"key":"10603_CR12","doi-asserted-by":"crossref","unstructured":"Ji P, Salzmann M, Li H (2016) Shape interaction matrix revisited and robustified: efficient subspace clustering with corrupted and incomplete data. In: IEEE international conference on computer vision, pp 4687\u20134695","DOI":"10.1109\/ICCV.2015.532"},{"key":"10603_CR13","unstructured":"Kanatani K (2001) Motion segmentation by subspace separation and model selection. In: Eighth IEEE international conference on computer vision, 2001. ICCV 2001. Proceedings, vol 2, pp 586\u2013591"},{"key":"10603_CR14","doi-asserted-by":"crossref","unstructured":"Kumar S, Dai Y, Li H (2017) Spatio-temporal union of subspaces for multi-body non-rigid structure-from-motion. Pattern Recogn 71:428\u2013443","DOI":"10.1016\/j.patcog.2017.05.014"},{"issue":"1","key":"10603_CR15","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","volume":"35","author":"L Guangcan","year":"2013","unstructured":"Guangcan L, Zhouchen L, Shuicheng Y, Sun J, Yong Y, Yi M (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"},{"key":"10603_CR16","unstructured":"Liu G, Lin Z, Yu Y (2010) Robust subspace segmentation by low-rank representation. In: International conference on machine learning, pp 663\u2013670"},{"key":"10603_CR17","unstructured":"Guangcan L, Shuicheng Y (2011) Latent low-rank representation for subspace segmentation and feature extraction. In: IEEE international conference on computer vision, ICCV 2011, pp 1615\u20131622"},{"issue":"9","key":"10603_CR18","doi-asserted-by":"publisher","first-page":"4022","DOI":"10.1109\/TIP.2014.2343458","volume":"23","author":"L Junmin","year":"2014","unstructured":"Junmin L, Yijun C, Jiang-She Z, Zongben X (2014) Enhancing low-rank subspace clustering by manifold regularization. IEEE Trans Image Process 23(9):4022\u20134030","journal-title":"IEEE Trans Image Process"},{"key":"10603_CR19","doi-asserted-by":"crossref","unstructured":"Lu CY, Min H, Zhao ZQ, Zhu L, De Huang S, Yan S (2012) Robust and efficient subspace segmentation via least squares regression. In: European conference on computer vision, pp 347\u2013360","DOI":"10.1007\/978-3-642-33786-4_26"},{"key":"10603_CR20","unstructured":"Nene SA, Nayar SK, Murase H, et\u00a0al. (1996) Columbia object image library (coil-100)"},{"key":"10603_CR21","unstructured":"Nie F, Huang H (2016) Subspace clustering via new low-rank model with discrete group structure constraint. In: Proceedings of the international joint conference on artificial intelligence, pp 1874\u20131880"},{"key":"10603_CR22","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. ACM, pp 977\u2013986","DOI":"10.1145\/2623330.2623726"},{"key":"10603_CR23","doi-asserted-by":"crossref","unstructured":"Peng X, Zhang L, Yi Z (2013) Scalable sparse subspace clustering. In: IEEE conference on computer vision and pattern recognition, pp 430\u2013437","DOI":"10.1109\/CVPR.2013.62"},{"issue":"8","key":"10603_CR24","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"S Jianbo","year":"2000","unstructured":"Jianbo S, Jitendra M (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":"10603_CR25","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1162\/089976699300016728","volume":"11","author":"ME Tipping","year":"1999","unstructured":"Tipping ME, Bishop CM (1999) Mixtures of probabilistic principal component analyzers. Neural Comput 11(2):443\u2013482","journal-title":"Neural Comput"},{"issue":"1","key":"10603_CR26","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1023\/A:1004678431677","volume":"105","author":"P Tseng","year":"2000","unstructured":"Tseng P (2000) Nearest q-flat to m points. J Optim Theory Appl 105(1):249\u2013252","journal-title":"J Optim Theory Appl"},{"key":"10603_CR27","doi-asserted-by":"crossref","unstructured":"Wang P, Han B, Li J, Gao X (2018) Structural reweight sparse subspace clustering. Neural processing letters, pp 1\u201313","DOI":"10.1007\/s11063-018-9859-8"},{"key":"10603_CR28","volume-title":"Face recognition: from theory to applications","author":"H Wechsler","year":"2012","unstructured":"Wechsler H, Phillips JP, Bruce V, Soulie FF, Huang TS (2012) Face recognition: from theory to applications, vol 163. Springer, Berlin"},{"key":"10603_CR29","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.jvcir.2016.03.017","volume":"38","author":"W Lai","year":"2016","unstructured":"Lai W, Xiaofeng W, Jun Y, Aihua W (2016) Spectral clustering steered low-rank representation for subspace segmentation. J Vis Commun Image Represent 38:386\u2013395","journal-title":"J Vis Commun Image Represent"},{"key":"10603_CR30","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neunet.2018.08.007","volume":"108","author":"W Jie","year":"2018","unstructured":"Jie W, Fang Xiaozhao X, Yong TC, Lunke F (2018) Low-rank representation with adaptive graph regularization. Neural Netw 108:83\u201396","journal-title":"Neural Netw"},{"key":"10603_CR31","doi-asserted-by":"crossref","unstructured":"Wu Z, Yin M, Zhou Y, Fang X, Xie S (2017) Robust spectral subspace clustering based on least square regression. In: Neural processing letters, pp 1\u201314","DOI":"10.1007\/s11063-017-9726-z"},{"key":"10603_CR32","doi-asserted-by":"crossref","unstructured":"Yan J, Pollefeys M (2006) A general framework for motion segmentation: independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In: European conference on computer vision, ECCV, pp 94\u2013106","DOI":"10.1007\/11744085_8"},{"issue":"3","key":"10603_CR33","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1109\/TPAMI.2015.2462360","volume":"38","author":"Y Ming","year":"2016","unstructured":"Ming Y, Junbin G, Zhouchen L (2016) Laplacian regularized low-rank representation and its applications. IEEE Trans Pattern Anal Mach Intell 38(3):504\u2013517","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"10603_CR34","doi-asserted-by":"publisher","first-page":"4918","DOI":"10.1109\/TIP.2015.2472277","volume":"24","author":"Y Ming","year":"2015","unstructured":"Ming Y, Junbin G, Zhouchen L, Qinfeng S, Yi G (2015) Dual graph regularized latent low-rank representation for subspace clustering. IEEE Trans Image Process 24(12):4918\u20134933","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"10603_CR35","first-page":"217","volume":"100","author":"Z Teng","year":"2010","unstructured":"Teng Z, Arthur S, Yi W, Gilad L (2010) Hybrid linear modeling via local best-fit flats. Int J Comput Vis 100(3):217\u2013240","journal-title":"Int J Comput Vis"},{"key":"10603_CR36","unstructured":"Zhuang L, Gao H, Lin Z, Ma Y, Zhang X, Yu N (2012) Non-negative low rank and sparse graph for semi-supervised learning. In: 2012 IEEE conference on computer vision and pattern recognition, Providence, RI, USA, June 16\u201321, 2012, pp 2328\u20132335"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10603-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10603-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10603-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T10:16:15Z","timestamp":1635675375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10603-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,9]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["10603"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10603-w","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2021,8,9]]},"assertion":[{"value":"20 July 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}