{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T17:17:55Z","timestamp":1780507075696,"version":"3.54.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2018,6,20]],"date-time":"2018-06-20T00:00:00Z","timestamp":1529452800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Guangzhou Science and Technology Program projects","award":["201508010067"],"award-info":[{"award-number":["201508010067"]}]},{"name":"Guangzhou Science and Technology Program projects","award":["201604020145"],"award-info":[{"award-number":["201604020145"]}]},{"name":"Guangzhou Science and Technology Program projects","award":["2016201604030034"],"award-info":[{"award-number":["2016201604030034"]}]},{"name":"Guangzhou Science and Technology Program projects","award":["201604046017"],"award-info":[{"award-number":["201604046017"]}]},{"name":"Guangdong Provincial Natural Science Foundation","award":["17ZK0422"],"award-info":[{"award-number":["17ZK0422"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772141"],"award-info":[{"award-number":["61772141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402118"],"award-info":[{"award-number":["61402118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673123"],"award-info":[{"award-number":["61673123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Science and Technology Project","award":["2015B090901016"],"award-info":[{"award-number":["2015B090901016"]}]},{"name":"Guangdong Provincial Science and Technology Project","award":["2016B010108007"],"award-info":[{"award-number":["2016B010108007"]}]},{"name":"Guangdong Higher Education letter","award":["2015[133]"],"award-info":[{"award-number":["2015[133]"]}]},{"name":"Guangdong Higher Education letter","award":["2014[97]"],"award-info":[{"award-number":["2014[97]"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s13042-018-0849-y","type":"journal-article","created":{"date-parts":[[2018,6,20]],"date-time":"2018-06-20T06:53:33Z","timestamp":1529477613000},"page":"1731-1745","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Joint sparse representation and locality preserving projection for feature extraction"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8637-051X","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peipei","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozhao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyao","family":"Teng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,6,20]]},"reference":[{"key":"849_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18178\/ijmlc.2017.7.1.610","volume":"7","author":"R Shang","year":"2017","unstructured":"Shang R, Chang J, Jiao L, Xue Y (2017) Unsupervised feature selection based on self-representation sparse regression and local similarity preserving. Int J Mach Learn Cybern 7:1\u201314","journal-title":"Int J Mach Learn Cybern"},{"key":"849_CR2","unstructured":"He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In: International Conference on Neural Information Processing Systems, MIT Press, pp 507\u2013514"},{"key":"849_CR3","doi-asserted-by":"publisher","unstructured":"Wen J, Fang X, Cui J, Fei L, Yan K, Chen Y, Xu Y (2018) Robust sparse linear discriminant analysis. IEEE Trans Circ Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2018.2799214","DOI":"10.1109\/TCSVT.2018.2799214"},{"issue":"6","key":"849_CR4","first-page":"793","volume":"44","author":"C Hou","year":"2017","unstructured":"Hou C, Nie F, Li X, Yi D, Wu Y (2017) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793\u2013804","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"849_CR5","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.neucom.2013.08.040","volume":"128","author":"X Fang","year":"2014","unstructured":"Fang X, Xu Y, Li X, Fan Z, Liu H, Chen Y (2014) Locality and similarity preserving embedding for feature selection. Neurocomputing 128(5):304\u2013315","journal-title":"Neurocomputing"},{"key":"849_CR6","unstructured":"Cai Z, Zhu W (2017) Multi-label feature selection via feature manifold learning and sparsity regularization. Int J Mach Learn Cybern, 1\u201314"},{"issue":"7","key":"849_CR7","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TIP.2010.2044958","volume":"19","author":"F Nie","year":"2010","unstructured":"Nie F, Xu D, Tsang I, Zhang C (2010) Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans Image Process 19(7):1921\u20131932","journal-title":"IEEE Trans Image Process"},{"key":"849_CR8","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.neucom.2013.01.036","volume":"113","author":"Y Xu","year":"2013","unstructured":"Xu Y, Zhu Q, Fan Z, Wang Y, Pan J (2013) From the idea of sparse representation to a representation-based transformation method for feature extraction. Neurocomputing 113:168\u2013176","journal-title":"Neurocomputing"},{"issue":"1","key":"849_CR9","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1162\/jocn.1991.3.1.71","volume":"3","author":"M Turk","year":"1991","unstructured":"Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3(1):71\u201386","journal-title":"J Cognit Neurosci"},{"key":"849_CR10","unstructured":"Belhumeur P, Hespanha J, Kriegman D (1996) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: European Conference on Computer Vision, pp 43\u201358"},{"key":"849_CR11","unstructured":"Bartlett M (1998) Independent component representations for face recognition. In: Proceedings of SPIE Symposium on Electronic Imaging, vol 3299"},{"issue":"8","key":"849_CR12","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"849_CR13","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/72.914517","volume":"12","author":"K Muller","year":"2001","unstructured":"Muller K, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12(2):181\u2013201","journal-title":"IEEE Trans Neural Netw"},{"key":"849_CR14","doi-asserted-by":"crossref","unstructured":"Yang M (2002) Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp 215\u2013220","DOI":"10.1109\/AFGR.2002.4527207"},{"issue":"10","key":"849_CR15","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1016\/j.patcog.2005.01.023","volume":"38","author":"J Yang","year":"2005","unstructured":"Yang J, Gao X, Zhang D, Yang J (2005) Kerenel ICA: an alternative formulation and its application to face recognition. Pattern Recognit 38(10):1784\u20131787","journal-title":"Pattern Recognit"},{"issue":"5500","key":"849_CR16","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"S Roweis","year":"2000","unstructured":"Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20136","journal-title":"Science"},{"key":"849_CR17","doi-asserted-by":"crossref","unstructured":"Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. MIT Press 15(6):1373\u20131396","DOI":"10.1162\/089976603321780317"},{"key":"849_CR18","unstructured":"He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. In: IEEE International Conference on Computer Vision, pp 2(23):1208\u20131213"},{"issue":"3","key":"849_CR19","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2005.55","volume":"27","author":"X He","year":"2005","unstructured":"He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328\u2013340","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"849_CR20","doi-asserted-by":"crossref","unstructured":"Pang Y, Zhang L, Liu Z, Yu N, Li H (2005) Neighborhood preserving projections (NPP): a novel linear dimension reduction method. In: International Conference on Advances in Intelligent Computing, pp 3644:117\u2013125","DOI":"10.1007\/11538059_13"},{"issue":"1","key":"849_CR21","doi-asserted-by":"publisher","first-page":"017202","DOI":"10.1117\/1.3067869","volume":"48","author":"F Nie","year":"2009","unstructured":"Nie F, Xiang S, Song Y, Zhang C (2009) Orthogonal locality minimizing globality maximizing projections for feature extraction. Opt Eng 48(1):017202","journal-title":"Opt Eng"},{"key":"849_CR22","doi-asserted-by":"crossref","unstructured":"Nie F, Cai X, Huang H (2014) Flexible shift-invariant locality and globality preserving projections. In: The European Conference on Machine Learning and Knowledge Discovery in Databases, pp 485\u2013500","DOI":"10.1007\/978-3-662-44851-9_31"},{"issue":"11","key":"849_CR23","doi-asserted-by":"publisher","first-page":"3733","DOI":"10.1109\/TCYB.2016.2578642","volume":"47","author":"Z Lai","year":"2017","unstructured":"Lai Z, Xu Y, Yang J, Shen L, Zhang D (2017) Rotational invariant dimensionality reduction algorithms. IEEE Trans Cybern 47(11):3733","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"849_CR24","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s13042-015-0380-3","volume":"8","author":"M Meng","year":"2015","unstructured":"Meng M, Wei J, Wang J, Ma Q, Wang X (2015) Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing. Int J Mach Learn Cybern 8(3):793\u2013805","journal-title":"Int J Mach Learn Cybern"},{"key":"849_CR25","unstructured":"Bengio Y, Vincent P, Delalleau O, Roux N, Ouimet M (2003) Out-of-sample extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral clustering. In: International Conference on Neural Information Processing Systems, pp 16:177\u2013184"},{"issue":"2","key":"849_CR26","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright J, Yang A, Arvind G, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210\u2013227","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"849_CR27","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/ACCESS.2015.2430359","volume":"3","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Xu Y, Yang J, Li X, Zhang D (2017) A survey of sparse representation: algorithms and applications. IEEE Access 3:490\u2013530","journal-title":"IEEE Access"},{"issue":"4","key":"849_CR28","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1109\/TNNLS.2015.2422994","volume":"27","author":"Z Lai","year":"2016","unstructured":"Lai Z, Wong W, Xu Y, Yang J, Zhang D (2016) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27(4):723\u2013735","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"849_CR29","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: IEEE Conference on Computer Vision and Pattern Recognition, pp 157(10):2328\u20132335"},{"key":"849_CR30","doi-asserted-by":"crossref","unstructured":"Nie F, Zhu W, Li X (2017) Unsupervised large graph embedding. In: Proceeding of the Thirty-First AAAI Conference on Artificial Intelligence, pp 2422\u20132428","DOI":"10.1609\/aaai.v31i1.10814"},{"key":"849_CR31","doi-asserted-by":"crossref","unstructured":"Yan S, Wang H (2009) Semi-supervised learning by sparse representation. In: Siam International Conference on Data Mining, 792\u2013801","DOI":"10.1137\/1.9781611972795.68"},{"issue":"1","key":"849_CR32","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.patcog.2009.05.005","volume":"43","author":"L Qiao","year":"2010","unstructured":"Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recognit 43(1):331\u2013341","journal-title":"Pattern Recognit"},{"key":"849_CR33","unstructured":"Liu Z, Yin J, Jin Z (2010) Locality preserving projections based on L1 Graph. Pattern Recognition, 1\u20134"},{"key":"849_CR34","doi-asserted-by":"crossref","unstructured":"Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 977\u2013986","DOI":"10.1145\/2623330.2623726"},{"issue":"6","key":"849_CR35","doi-asserted-by":"publisher","first-page":"2905","DOI":"10.1109\/TIP.2017.2691543","volume":"26","author":"W Wong","year":"2017","unstructured":"Wong W, Lai Z, Wen J, Fang X, Lu Y (2017) Low rank embedding for robust image feature extraction. IEEE Trans Image Process 26(6):2905\u20132917","journal-title":"IEEE Trans Image Process"},{"key":"849_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2017.01.001","volume":"88","author":"X Fang","year":"2017","unstructured":"Fang X, Xu Y, Li X, Lai Z, Teng S, Fei L (2017) Orthogonal self-guided similarity preserving projection for classification and clustering. Neural Netw 88:1\u20138","journal-title":"Neural Netw"},{"key":"849_CR37","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.patcog.2018.04.004","volume":"81","author":"J Wen","year":"2018","unstructured":"Wen J, Zhang B, Xu Y, Yang J, Han N (2018) Adaptive weighted nonnegative low-rank representation. Pattern Recognit 81:326\u2013340","journal-title":"Pattern Recognit"},{"key":"849_CR38","doi-asserted-by":"crossref","unstructured":"Baraniuk R (2007) A lecture on compressive sensing. IEEE Signal Processing Magazine, 1\u20139","DOI":"10.1109\/MSP.2007.4286571"},{"issue":"9","key":"849_CR39","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1162\/neco.2007.19.9.2301","volume":"19","author":"J Murray","year":"2007","unstructured":"Murray J, Kreutz-Delgado K (2007) Visual recognition and inference using dynamic overcomplete sparse learning. Neural Comput 19(9):2301","journal-title":"Neural Comput"},{"issue":"1\u20132","key":"849_CR40","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/S0304-3975(97)00115-1","volume":"209","author":"E Amaldi","year":"1998","unstructured":"Amaldi E, Kann V (1998) On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor Comput Sci 209(1\u20132):237\u2013260","journal-title":"Theor Comput Sci"},{"issue":"6","key":"849_CR41","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1002\/cpa.20132","volume":"59","author":"D Donoho","year":"2006","unstructured":"Donoho D (2006) For most large underdetermined systems of linear equations the minimal L1-norm solution is also the sparsest solution. Commun Pure Appl Math 59(6):797\u2013829","journal-title":"Commun Pure Appl Math"},{"issue":"2","key":"849_CR42","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/TIP.2015.2510498","volume":"25","author":"Y Xu","year":"2016","unstructured":"Xu Y, Fang X, Wu J, Li X, Zhang D (2016) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25(2):850\u2013863","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"849_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2010","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1\u2013122","journal-title":"Found Trends Mach Learn"},{"issue":"4","key":"849_CR44","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1137\/080738970","volume":"20","author":"J Cai","year":"2008","unstructured":"Cai J, Cands E, Shen Z (2008) A singular value thresholding algorithm for matrix completion. Siam J Optim 20(4):1956\u20131982","journal-title":"Siam J Optim"},{"issue":"6","key":"849_CR45","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1109\/34.927464","volume":"23","author":"A Georghiades","year":"2001","unstructured":"Georghiades A, Belhumeur P, Kriegman D (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643\u2013660","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"849_CR46","unstructured":"Martinez A (1998) The AR face database. Cvc Technical Report, 24"},{"key":"849_CR47","unstructured":"Nene S, Nayar S, Murase H (1996) Columbia object image library (COIL-100). Columbia University"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0849-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-018-0849-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0849-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:28:18Z","timestamp":1661473698000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-018-0849-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,20]]},"references-count":47,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["849"],"URL":"https:\/\/doi.org\/10.1007\/s13042-018-0849-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,20]]},"assertion":[{"value":"21 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}