{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:48Z","timestamp":1740108048841,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T00:00:00Z","timestamp":1661817600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T00:00:00Z","timestamp":1661817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71701132","61702336"],"award-info":[{"award-number":["71701132","61702336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"crossref","award":["JCYJ20200109110410133"],"award-info":[{"award-number":["JCYJ20200109110410133"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00521-022-07678-4","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T23:06:44Z","timestamp":1661900804000},"page":"22583-22599","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-expressiveness property-induced structured optimal graph for unsupervised feature selection"],"prefix":"10.1007","volume":"34","author":[{"given":"Hainan","family":"Guo","sequence":"first","affiliation":[]},{"given":"Haowen","family":"Xia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3224-0063","authenticated-orcid":false,"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"7678_CR1","doi-asserted-by":"crossref","unstructured":"AlSukker A, Khushaba RN, Al-Ani A (2010) Enhancing the diversity of genetic algorithm for improved feature selection. In: 2010 IEEE international conference on systems, man and cybernetics, IEEE, pp 1325\u20131331","DOI":"10.1109\/ICSMC.2010.5642445"},{"issue":"7","key":"7678_CR2","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/34.598228","volume":"19","author":"PN Belhumeur","year":"1997","unstructured":"Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711\u2013720","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7678_CR3","unstructured":"Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Nips, pp 585\u2013591"},{"key":"7678_CR4","doi-asserted-by":"crossref","unstructured":"Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 333\u2013342","DOI":"10.1145\/1835804.1835848"},{"issue":"1","key":"7678_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16\u201328","journal-title":"Comput Electr Eng"},{"key":"7678_CR6","doi-asserted-by":"crossref","unstructured":"Cole R, Fanty M (1990) Spoken letter recognition. In: Speech and natural language: proceedings of a workshop held at Hidden Valley, Pennsylvania, pp 24\u201327","DOI":"10.3115\/116580.116725"},{"key":"7678_CR7","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.neucom.2019.10.018","volume":"378","author":"D Ding","year":"2020","unstructured":"Ding D, Yang X, Xia F et al (2020) Unsupervised feature selection via adaptive hypergraph regularized latent representation learning. Neurocomputing 378:79\u201397","journal-title":"Neurocomputing"},{"issue":"9","key":"7678_CR8","doi-asserted-by":"publisher","first-page":"2777","DOI":"10.1007\/s00500-017-2748-7","volume":"22","author":"J Ding","year":"2018","unstructured":"Ding J, He X, Yuan J et al (2018) Automatic clustering based on density peak detection using generalized extreme value distribution. Soft Comput 22(9):2777\u20132796","journal-title":"Soft Comput"},{"issue":"11","key":"7678_CR9","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":"7678_CR10","unstructured":"Ester M, Kriegel HP, Sander J et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: kdd, pp 226\u2013231"},{"issue":"11","key":"7678_CR11","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 i. Proc Natl Acad Sci USA 35(11):652","journal-title":"Proc Natl Acad Sci USA"},{"key":"7678_CR12","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"key":"7678_CR13","unstructured":"Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning"},{"key":"7678_CR14","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.knosys.2017.10.028","volume":"140","author":"E Hancer","year":"2018","unstructured":"Hancer E, Xue B, Zhang M (2018) Differential evolution for filter feature selection based on information theory and feature ranking. Knowl Based Syst 140:103\u2013119","journal-title":"Knowl Based Syst"},{"issue":"6","key":"7678_CR15","first-page":"793","volume":"44","author":"C Hou","year":"2013","unstructured":"Hou C, Nie F, Li X et al (2013) 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":"7678_CR16","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull JJ (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16(5):550\u2013554","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"7678_CR17","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/89044","volume":"7","author":"J Khan","year":"2001","unstructured":"Khan J, Wei JS, Ringner M et al (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(6):673\u2013679","journal-title":"Nat Med"},{"key":"7678_CR18","doi-asserted-by":"crossref","unstructured":"Lee M, Lee J, Lee H et\u00a0al (2015) Membership representation for detecting block-diagonal structure in low-rank or sparse subspace clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1648\u20131656","DOI":"10.1109\/CVPR.2015.7298773"},{"issue":"5","key":"7678_CR19","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1109\/TNNLS.2018.2868847","volume":"30","author":"X Li","year":"2018","unstructured":"Li X, Zhang H, Zhang R et al (2018) Generalized uncorrelated regression with adaptive graph for unsupervised feature selection. IEEE Trans Neural Netw Learn Syst 30(5):1587\u20131595","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"7678_CR20","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/s10115-017-1059-8","volume":"53","author":"Y Li","year":"2017","unstructured":"Li Y, Li T, Liu H (2017) Recent advances in feature selection and its applications. Knowl Inf Syst 53(3):551\u2013577","journal-title":"Knowl Inf Syst"},{"issue":"4","key":"7678_CR21","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1093\/biomet\/74.4.817","volume":"74","author":"NT Longford","year":"1987","unstructured":"Longford NT (1987) A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometrika 74(4):817\u2013827","journal-title":"Biometrika"},{"issue":"12","key":"7678_CR22","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1109\/34.817413","volume":"21","author":"MJ Lyons","year":"1999","unstructured":"Lyons MJ, Budynek J, Akamatsu S (1999) Automatic classification of single facial images. IEEE Trans Pattern Anal Mach Intell 21(12):1357\u20131362","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7678_CR23","doi-asserted-by":"crossref","unstructured":"Ma Z, Yang Y, Nie F et\u00a0al (2011) Exploiting the entire feature space with sparsity for automatic image annotation. In: Proceedings of the 19th ACM international conference on multimedia, pp 283\u2013292","DOI":"10.1145\/2072298.2072336"},{"issue":"6","key":"7678_CR24","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/TMM.2012.2199293","volume":"14","author":"Z Ma","year":"2012","unstructured":"Ma Z, Nie F, Yang Y et al (2012) Discriminating joint feature analysis for multimedia data understanding. IEEE Trans Multimedia 14(6):1662\u20131672","journal-title":"IEEE Trans Multimedia"},{"key":"7678_CR25","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1016\/j.procs.2016.07.111","volume":"91","author":"J Miao","year":"2016","unstructured":"Miao J, Niu L (2016) A survey on feature selection. Procedia Comput Sci 91:919\u2013926","journal-title":"Procedia Comput Sci"},{"key":"7678_CR26","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.asoc.2016.01.044","volume":"43","author":"P Moradi","year":"2016","unstructured":"Moradi P, Gholampour M (2016) A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl Soft Comput 43:117\u2013130","journal-title":"Appl Soft Comput"},{"key":"7678_CR27","unstructured":"Nene S, Nayar S, Murase H et\u00a0al (1996) Columbia object image library (coil-20)"},{"key":"7678_CR28","unstructured":"Nie F, Huang H, Cai X et al (2010) Efficient and robust feature selection via joint $${{\\ell }_{2,1}}$$-norms minimization. Adv Neural Inf Process Syst 23"},{"key":"7678_CR29","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":"7678_CR30","doi-asserted-by":"crossref","unstructured":"Nie F, Zhu W, Li X (2016) Unsupervised feature selection with structured graph optimization. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10168"},{"issue":"302","key":"7678_CR31","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1080\/14786440009463897","volume":"50","author":"KX Pearson","year":"1900","unstructured":"Pearson KX (1900) On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond Edinb Dublin Philos Mag J Sci 50(302):157\u2013175","journal-title":"Lond Edinb Dublin Philos Mag J Sci"},{"issue":"12","key":"7678_CR32","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1109\/TNNLS.2015.2490080","volume":"27","author":"X Peng","year":"2015","unstructured":"Peng X, Tang H, Zhang L et al (2015) A unified framework for representation-based subspace clustering of out-of-sample and large-scale data. IEEE Trans Neural Netw Learn Syst 27(12):2499\u20132512","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7678_CR33","unstructured":"Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, Citeseer, pp 137\u2013143"},{"issue":"10","key":"7678_CR34","doi-asserted-by":"publisher","first-page":"3801","DOI":"10.1109\/TNNLS.2019.2946329","volume":"31","author":"P Ren","year":"2020","unstructured":"Ren P, Xiao Y, Chang X et al (2020) Structured optimal graph-based clustering with flexible embedding. IEEE Trans Neural Netw Learn Syst 31(10):3801\u20133813","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6191","key":"7678_CR35","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492\u20131496","journal-title":"Science"},{"key":"7678_CR36","doi-asserted-by":"crossref","unstructured":"Sanguinetti G, Laidler J, Lawrence ND (2005) Automatic determination of the number of clusters using spectral algorithms. In: 2005 IEEE workshop on machine learning for signal processing, IEEE, pp 55\u201360","DOI":"10.1109\/MLSP.2005.1532874"},{"issue":"3","key":"7678_CR37","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.imavis.2013.12.013","volume":"32","author":"C Shi","year":"2014","unstructured":"Shi C, Ruan Q, An G (2014) Sparse feature selection based on graph Laplacian for web image annotation. Image Vis Comput 32(3):189\u2013201","journal-title":"Image Vis Comput"},{"key":"7678_CR38","doi-asserted-by":"crossref","unstructured":"Shi L, Du L, Shen YD (2014b) Robust spectral learning for unsupervised feature selection. In: 2014 IEEE international conference on data mining, IEEE, pp 977\u2013982","DOI":"10.1109\/ICDM.2014.58"},{"key":"7678_CR39","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.engappai.2014.03.007","volume":"32","author":"S Tabakhi","year":"2014","unstructured":"Tabakhi S, Moradi P, Akhlaghian F (2014) An unsupervised feature selection algorithm based on ant colony optimization. Eng Appl Artif Intell 32:112\u2013123","journal-title":"Eng Appl Artif Intell"},{"issue":"106","key":"7678_CR40","first-page":"847","volume":"219","author":"F Wang","year":"2021","unstructured":"Wang F, Zhu L, Li J et al (2021) Unsupervised soft-label feature selection. Knowl Based Syst 219(106):847","journal-title":"Knowl Based Syst"},{"issue":"6","key":"7678_CR41","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TIP.2018.2810515","volume":"27","author":"W Wang","year":"2018","unstructured":"Wang W, Yan Y, Nie F et al (2018) Flexible manifold learning with optimal graph for image and video representation. IEEE Trans Image Process 27(6):2664\u20132675","journal-title":"IEEE Trans Image Process"},{"key":"7678_CR42","unstructured":"Yang Y, Shen HT, Ma Z, et\u00a0al (2011) $${{\\ell }_{2,1}}$$-norm regularized discriminative feature selection for unsupervised learning. In: IJCAI international joint conference on artificial intelligence"},{"key":"7678_CR43","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.knosys.2017.07.010","volume":"133","author":"L Yaohui","year":"2017","unstructured":"Yaohui L, Zhengming M, Fang Y (2017) Adaptive density peak clustering based on k-nearest neighbors with aggregating strategy. Knowl Based Syst 133:208\u2013220","journal-title":"Knowl Based Syst"},{"key":"7678_CR44","unstructured":"Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th international conference on machine learning (ICML-03), pp 856\u2013863"},{"issue":"8","key":"7678_CR45","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2010.215","volume":"33","author":"H Zeng","year":"2010","unstructured":"Zeng H, Ym C (2010) Feature selection and kernel learning for local learning-based clustering. IEEE Trans Pattern Anal Mach Intell 33(8):1532\u20131547","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"7678_CR46","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1007\/s12559-021-09875-0","volume":"14","author":"H Zhao","year":"2022","unstructured":"Zhao H, Li Q, Wang Z et al (2022) Joint adaptive graph learning and discriminative analysis for unsupervised feature selection. Cogn Comput 14(3):1211\u20131221","journal-title":"Cogn Comput"},{"issue":"1","key":"7678_CR47","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s11063-020-10383-9","volume":"53","author":"J Zhu","year":"2021","unstructured":"Zhu J, Jang-Jaccard J, Liu T et al (2021) Joint spectral clustering based on optimal graph and feature selection. Neural Process Lett 53(1):257\u2013273","journal-title":"Neural Process Lett"},{"issue":"3","key":"7678_CR48","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TKDE.2017.2763618","volume":"30","author":"X Zhu","year":"2017","unstructured":"Zhu X, Zhang S, Hu R et al (2017) Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans Knowl Data Eng 30(3):517\u2013529","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"7678_CR49","first-page":"3016","volume":"34","author":"X Zhu","year":"2022","unstructured":"Zhu X, Zhang S, Zhu Y et al (2022) Unsupervised spectral feature selection with dynamic hyper-graph learning. IEEE Trans Knowl Data Eng 34(6):3016\u20133028","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07678-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07678-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07678-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:22:43Z","timestamp":1667866963000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07678-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,30]]},"references-count":49,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["7678"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07678-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,8,30]]},"assertion":[{"value":"23 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}