{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T10:42:32Z","timestamp":1693132952526},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2017,9,30]],"date-time":"2017-09-30T00:00:00Z","timestamp":1506729600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s11042-017-5235-3","type":"journal-article","created":{"date-parts":[[2017,9,30]],"date-time":"2017-09-30T00:50:21Z","timestamp":1506732621000},"page":"29551-29572","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hypergraph expressing low-rank feature selection algorithm"],"prefix":"10.1007","volume":"77","author":[{"given":"Yue","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangding","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuelian","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,9,30]]},"reference":[{"key":"5235_CR1","unstructured":"Cai X, Nie F, Huang H (2013) Exact top-k feature selection via l 2,0 -norm constraint. Int Joint Conf Artif Intell:1240\u20131246"},{"key":"5235_CR2","doi-asserted-by":"crossref","unstructured":"Chang X, Nie F, Yang Y, Huang H (2014) A convex formulation for semi-supervised multi-label feature selection. Twenty-Eighth AAAI Conf Artif Intell:1171\u20131177","DOI":"10.1609\/aaai.v28i1.8922"},{"key":"5235_CR3","unstructured":"Chen H-T, Chang H-W, Liu T-L (2005) Local Discriminant Embedding and Its Variants., Local Discriminant Embedding and Its Variants, IEEE Computer Society Conference on Computer Vision & Pattern Recognition"},{"key":"5235_CR4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.eswa.2016.01.021","volume":"53","author":"BZ Dadaneh","year":"2016","unstructured":"Dadaneh BZ, Markid HY, Zakerolhosseini A (2016) Unsupervised probabilistic feature selection using ant colony optimization. Expert Syst Appl Int J 53:27\u201342","journal-title":"Expert Syst Appl Int J"},{"key":"5235_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/cpa.20303","volume":"63","author":"I Daubechies","year":"2008","unstructured":"Daubechies I, Devore R, Fornasier M, G\u00fcnt\u00fcrk CS (2008) Iteratively reweighted least squares minimization for sparse recovery. Commun Pure Appl Math 63:1\u201338","journal-title":"Commun Pure Appl Math"},{"key":"5235_CR6","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.sigpro.2014.12.027","volume":"120","author":"X Du","year":"2014","unstructured":"Du X, Yan Y, Pan P, Long G, Zhao L (2014) Multiple graph unsupervised feature selection. Signal Process 120:754\u2013760","journal-title":"Signal Process"},{"key":"5235_CR7","first-page":"209","volume":"37","author":"L Du","year":"2015","unstructured":"Du L, Shen YD (2015) Unsupervised feature selection with adaptive structure learning. Comput Sci 37:209\u2013218","journal-title":"Comput Sci"},{"key":"5235_CR8","unstructured":"Feng S, Lu H, Long X (2015) Discriminative Dictionary Learning Based on Supervised Feature Selection for Image Classification. Seventh Int Symp Comput Intell Des:225\u2013228"},{"key":"5235_CR9","first-page":"186","volume":"16","author":"X He","year":"2003","unstructured":"He X, Niyogi P (2003) Locality preserving projections. Advx Neural Inf Process Syst 16:186\u2013197","journal-title":"Advx Neural Inf Process Syst"},{"key":"5235_CR10","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1109\/TCYB.2013.2272642","volume":"44","author":"C Hou","year":"2014","unstructured":"Hou C, Nie F, Li X, Yi D (2014) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44:793","journal-title":"IEEE Trans Cybern"},{"key":"5235_CR11","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.neucom.2016.05.081","volume":"220","author":"R Hu","year":"2017","unstructured":"Hu R, Zhu X, Cheng D, He W, Yan Y, Song J, Zhang S (2017) Graph self-representation method for unsupervised feature selection. Neurocomputing 220:130\u2013137","journal-title":"Neurocomputing"},{"key":"5235_CR12","doi-asserted-by":"crossref","unstructured":"Ling C, Yang Q, Wang J, Zhang S (2004) Decision trees with minimal costs, Proceedings of 21st International Conference on Machine Learning (ICML)","DOI":"10.1145\/1015330.1015369"},{"key":"5235_CR13","doi-asserted-by":"crossref","unstructured":"Li J, Hu X, Wu L, Liu H (2016) Robust unsupervised feature selection on networked data, Siam Int Conf Data Mining:387\u2013395","DOI":"10.1137\/1.9781611974348.44"},{"key":"5235_CR14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.neucom.2012.05.031","volume":"105","author":"Y Liu","year":"2013","unstructured":"Liu Y, Nie F, Wu J, Chen L (2013) Efficient semi-supervised feature selection with noise insensitive trace ratio criterion. Neurocomputing 105:12\u201318","journal-title":"Neurocomputing"},{"key":"5235_CR15","doi-asserted-by":"crossref","unstructured":"Luo D, Ding CHQ, Huang H (2011) Linear discriminant analysis: new formulations and overfit analysis, AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v25i1.7926"},{"key":"5235_CR16","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1109\/TMM.2012.2187179","volume":"14","author":"Z Ma","year":"2012","unstructured":"Ma Z, Nie F, Yang Y, Uijlings JRR (2012) Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans Multimed 14:1021\u20131030","journal-title":"IEEE Trans Multimed"},{"key":"5235_CR17","unstructured":"Nie F, Yuan J, Huang H (2014) Optimal mean robust principal component analysis. Int Conf Mach Learn"},{"key":"5235_CR18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10489-006-0032-0","volume":"27","author":"Y Qin","year":"2007","unstructured":"Qin Y, Zhang S, Zhu X, Zhang J, Zhang C (2007) Semi-parametric optimization for missing data imputation. Appl Intell 27:79\u201388","journal-title":"Appl Intell"},{"key":"5235_CR19","doi-asserted-by":"crossref","unstructured":"Shi C, An G, Zhao R, Ruan Q (2016) Multi-view hessian semi-supervised sparse feature selection for multimedia analysis. IEEE Trans Circ Syst Video Technol 27:1947\u20131961","DOI":"10.1109\/TCSVT.2016.2576919"},{"key":"5235_CR20","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.is.2011.10.009","volume":"37","author":"T Wang","year":"2012","unstructured":"Wang T, Qin Z, Zhang S, Zhang C (2012) Cost-sensitive classification with inadequate labeled data. Inf Syst 37:508\u2013516","journal-title":"Inf Syst"},{"key":"5235_CR21","doi-asserted-by":"crossref","unstructured":"Wang XD, Chen RC, Yan F, Zeng ZQ (2016) Semi-supervised feature selection with exploiting shared information among multiple tasks. J of Vis Commun Image Represent 41:272\u2013280","DOI":"10.1016\/j.jvcir.2016.10.007"},{"key":"5235_CR22","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/TKDE.2003.1185839","volume":"15","author":"X Wu","year":"2003","unstructured":"Wu X, Zhang S (2003) Synthesizing high-frequency rules from different data sources. IEEE Trans Knowl Data Eng 15:353\u2013367","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5235_CR23","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/1010614.1010616","volume":"22","author":"X Wu","year":"2004","unstructured":"Wu X, Zhang C, Zhang S (2004) Efficient mining of both positive and negative association rules. ACM Trans Inf Syst 22:381\u2013405","journal-title":"ACM Trans Inf Syst"},{"key":"5235_CR24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.is.2003.10.001","volume":"30","author":"X Wu","year":"2005","unstructured":"Wu X, Zhang C, Zhang S (2005) Database classification for multi-database mining. Inf Syst 30:71\u201388","journal-title":"Inf Syst"},{"key":"5235_CR25","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/TSMCA.2002.804793","volume":"32","author":"S Zhang","year":"2002","unstructured":"Zhang S, Zhang C (2002) Anytime mining for multi-user applications. IEEE Trans Syst Man Cybern (Part A) 32:515\u2013521","journal-title":"IEEE Trans Syst Man Cybern (Part A)"},{"key":"5235_CR26","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/S0306-4379(02)00079-0","volume":"28","author":"S Zhang","year":"2003","unstructured":"Zhang S, Zhang C (2003) PostMining: maintenance of association rules by weighting. Inf Syst 28: 691\u2013707","journal-title":"Inf Syst"},{"key":"5235_CR27","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TMM.2012.2237023","volume":"15","author":"Y Yang","year":"2013","unstructured":"Yang Y, Ma Z, Hauptmann AG, Sebe N (2013) Feature selection for multimedia analysis by sharing information among multiple tasks. IEEE Trans Multimed 15:661\u2013669","journal-title":"IEEE Trans Multimed"},{"key":"5235_CR28","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1109\/TKDE.2005.188","volume":"17","author":"S Zhang","year":"2005","unstructured":"Zhang S, Qin Z, Ling CX, Sheng S (2005) Missing Is useful: missing values in cost-sensitive decision trees. IEEE Trans Knowl Data Eng 17:1689\u20131693","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5235_CR29","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1109\/TKDE.2005.188","volume":"17","author":"S Zhang","year":"2005","unstructured":"Zhang S, Qin Z, Ling C (2005) Missing is useful: missing values in cost-sensitive decision trees. IEEE Trans Knowl Data Eng 17:1689\u20131693","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5235_CR30","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10489-009-0207-6","volume":"35","author":"S Zhang","year":"2011","unstructured":"Zhang S (2011) Shell-neighbor method and its application in missing data imputation. Appl Intell 35:123\u2013133","journal-title":"Appl Intell"},{"key":"5235_CR31","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.jss.2010.11.887","volume":"84","author":"S Zhang","year":"2011","unstructured":"Zhang S, Jin Z, Zhu X (2011) Missing data imputation by utilizing information within incomplete instances. J Syst Softw 84:452\u2013459","journal-title":"J Syst Softw"},{"key":"5235_CR32","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","volume":"85","author":"S Zhang","year":"2012","unstructured":"Zhang S (2012) Nearest neighbor selection for iteratively kNN imputation. J Syst Softw 85:2541\u20132552","journal-title":"J Syst Softw"},{"key":"5235_CR33","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.jss.2011.10.007","volume":"85","author":"S Zhang","year":"2012","unstructured":"Zhang S (2012) Decision tree classifiers sensitive to heterogeneous costs. J Syst Softw 85:771\u2013779","journal-title":"J Syst Softw"},{"key":"5235_CR34","doi-asserted-by":"crossref","unstructured":"Zhang S, Li X, Zong M, Zhu X, Wang R (2017) Efficient kNN classification with different numbers of nearest neighbors. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2017.2673241"},{"key":"5235_CR35","first-page":"43","volume":"8","author":"S Zhang","year":"2017","unstructured":"Zhang S, Li X, Zong M, Zhu X, Cheng D (2017) Learning k for knn classification. ACM Trans Intell Syst Technol 8:43","journal-title":"ACM Trans Intell Syst Technol"},{"key":"5235_CR36","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/TKDE.2006.30","volume":"18","author":"Y Zhao","year":"2006","unstructured":"Zhao Y, Zhang S (2006) Generalized dimension-reduction framework for recent-biased time series analysis. IEEE Trans Knowl Data Eng 18:231\u2013244","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5235_CR37","doi-asserted-by":"crossref","unstructured":"Zhao Z, Wang L, Liu H (2011) Efficient spectral feature selection with minimum redundancy, Twenty-Fourth AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v24i1.7671"},{"key":"5235_CR38","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TKDE.2010.99","volume":"23","author":"X Zhu","year":"2011","unstructured":"Zhu X, Zhang* S, Jin Z, Zhang Z, Xu Z (2011) Missing value estimation for mixed-attribute datasets. IEEE Trans Knowl Data Eng 23:110\u2013121","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5235_CR39","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1016\/j.patcog.2012.02.007","volume":"45","author":"X Zhu","year":"2012","unstructured":"Zhu X, Huang Z, Shen Heng T, Cheng J, Xu C (2012) Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recogn 45:3003\u20133016","journal-title":"Pattern Recogn"},{"key":"5235_CR40","doi-asserted-by":"crossref","unstructured":"Zhu X, Huang Z, Shen HT, Zhao X (2013) Linear cross-modal hashing for efficient multimedia search: 143-152","DOI":"10.1145\/2502081.2502107"},{"key":"5235_CR41","first-page":"9","volume":"31","author":"X Zhu","year":"2013","unstructured":"Zhu X, Huang Z, Cheng H, Cui J, Shen HT (2013) Sparse hashing for fast multimedia search. ACM Trans Inf Syst 31:9","journal-title":"ACM Trans Inf Syst"},{"key":"5235_CR42","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1109\/TIP.2014.2332764","volume":"23","author":"X Zhu","year":"2014","unstructured":"Zhu X, Zhang L, Huang Z (2014) A sparse embedding and least variance encoding approach to hashing. IEEE Trans Image Process 23:3737\u20133750","journal-title":"IEEE Trans Image Process"},{"key":"5235_CR43","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.neucom.2015.05.119","volume":"173","author":"Z Zeng","year":"2015","unstructured":"Zeng Z, Wang X, Zhang J, Wu Q (2015) Semi-supervised feature selection based on local discriminative information. Neurocomputing 173:102\u2013109","journal-title":"Neurocomputing"},{"key":"5235_CR44","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.patcog.2014.08.006","volume":"48","author":"P Zhu","year":"2015","unstructured":"Zhu P, Zuo W, Zhang L, Hu Q, Shiu SCK (2015) Unsupervised feature selection by regularized self-representation. Pattern Recogn 48:438\u2013446","journal-title":"Pattern Recogn"},{"key":"5235_CR45","doi-asserted-by":"crossref","unstructured":"Zhu P, Zhu W, Wang W, Zuo W, Hu Q (2016) Non-convex regularized self-representation for unsupervised feature selection *","DOI":"10.1016\/j.imavis.2016.11.014"},{"key":"5235_CR46","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TCYB.2015.2403356","volume":"46","author":"X Zhu","year":"2016","unstructured":"Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46:450\u2013461","journal-title":"IEEE Trans Cybern"},{"key":"5235_CR47","doi-asserted-by":"crossref","unstructured":"Zhu X, Suk H, Lee S-W, Shen D (2016) Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans Biomed Eng 63:607\u2013618","DOI":"10.1109\/TBME.2015.2466616"},{"key":"5235_CR48","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TCYB.2015.2403356","volume":"46","author":"X Zhu","year":"2016","unstructured":"Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46:450\u2013461","journal-title":"IEEE Trans Cybern"},{"key":"5235_CR49","doi-asserted-by":"crossref","unstructured":"Zhu X, Li X, Zhang S, Xu Z, Yu L, Wang C (2017) Graph PCA Hashing for Similarity Search, IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2017.2703636"},{"key":"5235_CR50","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.media.2015.10.008","volume":"38","author":"X Zhu","year":"2017","unstructured":"Zhu X, Suk H-I, Wang L, Lee S-W, Shen D (2017) A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med Image Anal 38:205\u2013214","journal-title":"Med Image Anal"},{"key":"5235_CR51","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TNNLS.2016.2521602","volume":"28","author":"X Zhu","year":"2017","unstructured":"Zhu X, Li X, Zhang S, Ju C, Wu X (2017) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans Neural Netw Learn Syst 28:1263\u20131275","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5235_CR52","doi-asserted-by":"crossref","unstructured":"Zhu X, Suk H-I, Huang H, Shen D (2017) Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers. IEEE Transactions on Big Data","DOI":"10.1109\/TBDATA.2017.2735991"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-017-5235-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5235-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5235-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T16:15:46Z","timestamp":1693066546000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-017-5235-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,30]]},"references-count":52,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["5235"],"URL":"https:\/\/doi.org\/10.1007\/s11042-017-5235-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,30]]}}}