{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:20:42Z","timestamp":1784550042840,"version":"3.55.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11432-022-3579-1","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T09:02:32Z","timestamp":1683104552000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":170,"title":["Unsupervised feature selection via multiple graph fusion and feature weight learning"],"prefix":"10.1007","volume":"66","author":[{"given":"Chang","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"En","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"3579_CR1","doi-asserted-by":"crossref","unstructured":"Indyk P, Motwani R. Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing, 1998. 604\u2013613","DOI":"10.1145\/276698.276876"},{"key":"3579_CR2","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res, 2003, 3: 1157\u20131182","journal-title":"J Mach Learn Res"},{"key":"3579_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20192-9","volume-title":"Statistics for High-Dimensional Data: Methods, Theory and Applications","author":"P Buhlmann","year":"2011","unstructured":"Buhlmann P, van de Geer S. Statistics for High-Dimensional Data: Methods, Theory and Applications. Berlin: Springer, 2011"},{"key":"3579_CR4","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neunet.2018.03.004","volume":"103","author":"Y Shi","year":"2018","unstructured":"Shi Y, Liu J, Qi Z, et al. Learning from label proportions on high-dimensional data. Neural Networks, 2018, 103: 9\u201318","journal-title":"Neural Networks"},{"key":"3579_CR5","unstructured":"Song S, Steinke T, Thakkar O, et al. Evading the curse of dimensionality in unconstrained private GLMs. In: Proceedings of International Conference on Artificial Intelligence and Statistics, 2021. 2638\u20132646"},{"key":"3579_CR6","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1109\/TMM.2018.2889560","volume":"21","author":"C Tang","year":"2019","unstructured":"Tang C, Zhu X, Liu X, et al. Learning a joint affinity graph for multiview subspace clustering. IEEE Trans Multimedia, 2019, 21: 1724\u20131736","journal-title":"IEEE Trans Multimedia"},{"key":"3579_CR7","doi-asserted-by":"publisher","unstructured":"Tang C, Li Z, Wang J, et al. Unified one-step multi-view spectral clustering. IEEE Trans Knowl Data Eng, 2022. doi: https:\/\/doi.org\/10.1109\/TKDE.2022.3172687","DOI":"10.1109\/TKDE.2022.3172687"},{"key":"3579_CR8","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1109\/TNNLS.2013.2293418","volume":"25","author":"L Shao","year":"2014","unstructured":"Shao L, Liu L, Li X. Feature learning for image classification via multiobjective genetic programming. IEEE Trans Neural Netw Learn Syst, 2014, 25: 1359\u20131371","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR9","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2016.10.001","volume":"35","author":"B Pes","year":"2017","unstructured":"Pes B, Dess\u00ed N, Angioni M. Exploiting the ensemble paradigm for stable feature selection: a case study on high-dimensional genomic data. Inf Fusion, 2017, 35: 132\u2013147","journal-title":"Inf Fusion"},{"key":"3579_CR10","doi-asserted-by":"publisher","first-page":"3906","DOI":"10.1109\/TIP.2016.2570569","volume":"25","author":"L L C Kasun","year":"2016","unstructured":"Kasun L L C, Yang Y, Huang G B, et al. Dimension reduction with extreme learning machine. IEEE Trans Image Process, 2016, 25: 3906\u20133918","journal-title":"IEEE Trans Image Process"},{"key":"3579_CR11","doi-asserted-by":"crossref","unstructured":"Jolliffe I T. Principal component analysis and factor analysis. In: Proceedings of Principal Component Analysis, 1986. 115\u2013128","DOI":"10.1007\/978-1-4757-1904-8_7"},{"key":"3579_CR12","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/34.598228","volume":"19","author":"P N Belhumeur","year":"1997","unstructured":"Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Machine Intell, 1997, 19: 711\u2013720","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"3579_CR13","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TCSVT.2003.818352","volume":"14","author":"Q Liu","year":"2004","unstructured":"Liu Q, Lu H, Ma S. Improving kernel fisher discriminant analysis for face recognition. IEEE Trans Circuits Syst Video Technol, 2004, 14: 42\u201349","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"3579_CR14","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1198\/016214502760047131","volume":"97","author":"C Fraley","year":"2002","unstructured":"Fraley C, Raftery A E. Model-based clustering, discriminant analysis, and density estimation. J Am Statistical Assoc, 2002, 97: 611\u2013631","journal-title":"J Am Statistical Assoc"},{"key":"3579_CR15","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1109\/TSMCB.2005.863377","volume":"36","author":"W M Zuo","year":"2006","unstructured":"Zuo W M, Zhang D, Yang J, et al. BDPCA plus LDA: a novel fast feature extraction technique for face recognition. IEEE Trans Syst Man Cybern B, 2006, 36: 946\u2013953","journal-title":"IEEE Trans Syst Man Cybern B"},{"key":"3579_CR16","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1109\/TNNLS.2014.2306844","volume":"25","author":"Y Pang","year":"2014","unstructured":"Pang Y, Wang S, Yuan Y. Learning regularized LDA by clustering. IEEE Trans Neural Netw Learn Syst, 2014, 25: 2191\u20132201","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR17","doi-asserted-by":"publisher","first-page":"3390","DOI":"10.1109\/TKDE.2015.2455509","volume":"27","author":"M Banerjee","year":"2015","unstructured":"Banerjee M, Pal N R. Unsupervised feature selection with controlled redundancy (UFeSCoR). IEEE Trans Knowl Data Eng, 2015, 27: 3390\u20133403","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3579_CR18","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/34.990133","volume":"24","author":"P Mitra","year":"2002","unstructured":"Mitra P, Murthy C A, Pal S K. Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Machine Intell, 2002, 24: 301\u2013312","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"3579_CR19","first-page":"507","volume":"18","author":"X He","year":"2005","unstructured":"He X, Cai D, Niyogi P. Laplacian score for feature selection. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2005. 18: 507\u2013514","journal-title":"Proceedings of Annual Conference on Neural Information Processing Systems"},{"key":"3579_CR20","doi-asserted-by":"publisher","first-page":"5343","DOI":"10.1109\/TIP.2015.2479560","volume":"24","author":"Z Li","year":"2015","unstructured":"Li Z, Tang J. Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process, 2015, 24: 5343\u20135355","journal-title":"IEEE Trans Image Process"},{"key":"3579_CR21","doi-asserted-by":"crossref","unstructured":"Nie F, Wei Z, Li X. Unsupervised feature selection with structured graph optimization. In: Proceedings of AAAI Conference on Artificial Intelligence, 2016. 1302\u20131308","DOI":"10.1609\/aaai.v30i1.10168"},{"key":"3579_CR22","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.eswa.2017.11.053","volume":"96","author":"C Tang","year":"2018","unstructured":"Tang C, Zhu X, Chen J, et al. Robust graph regularized unsupervised feature selection. Expert Syst Appl, 2018, 96: 64\u201376","journal-title":"Expert Syst Appl"},{"key":"3579_CR23","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1109\/TKDE.2017.2681670","volume":"29","author":"C Hou","year":"2017","unstructured":"Hou C, Nie F, Tao H, et al. Multi-view unsupervised feature selection with adaptive similarity and view weight. IEEE Trans Knowl Data Eng, 2017, 29: 1998\u20132011","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3579_CR24","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1109\/TKDE.2019.2893638","volume":"32","author":"C Tang","year":"2020","unstructured":"Tang C, Liu X, Zhu X, et al. Feature selective projection with low-rank embedding and dual laplacian regularization. IEEE Trans Knowl Data Eng, 2020, 32: 1747\u20131760","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3579_CR25","doi-asserted-by":"publisher","first-page":"105417","DOI":"10.1016\/j.knosys.2019.105417","volume":"193","author":"P Zhou","year":"2020","unstructured":"Zhou P, Chen J, Fan M, et al. Unsupervised feature selection for balanced clustering. Knowledge-Based Syst, 2020, 193: 105417","journal-title":"Knowledge-Based Syst"},{"key":"3579_CR26","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1109\/TNNLS.2020.3042330","volume":"33","author":"R Zhang","year":"2022","unstructured":"Zhang R, Zhang Y, Li X. Unsupervised feature selection via adaptive graph learning and constraint. IEEE Trans Neural Netw Learn Syst, 2022, 33: 1355\u20131362","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR27","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/TGRS.2012.2200106","volume":"51","author":"W Z Liao","year":"2013","unstructured":"Liao W Z, Pizurica A, Scheunders P, et al. Semisupervised local discriminant analysis for feature extraction in hyperspectral images. IEEE Trans Geosci Remote Sens, 2013, 51: 184\u2013198","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3579_CR28","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1109\/TKDE.2013.86","volume":"26","author":"K Benabdeslem","year":"2014","unstructured":"Benabdeslem K, Hindawi M. Efficient semi-supervised feature selection: constraint, relevance, and redundancy. IEEE Trans Knowl Data Eng, 2014, 26: 1131\u20131143","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3579_CR29","doi-asserted-by":"crossref","unstructured":"Zhao Z, Liu H. Spectral feature selection for supervised and unsupervised learning. In: Proceedings of International Conference on Machine Learning, 2007. 1151\u20131157","DOI":"10.1145\/1273496.1273641"},{"key":"3579_CR30","unstructured":"Nie F, Huang H, Cai X, et al. Efficient and robust feature selection via joint \u21132,1-norms minimization. In: Proceedings of Annual Conference on Neural Information Processing Systems, 2010. 1813\u20131821"},{"key":"3579_CR31","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.patcog.2015.12.008","volume":"53","author":"N Zhou","year":"2016","unstructured":"Zhou N, Xu Y, Cheng H, et al. Global and local structure preserving sparse subspace learning: an iterative approach to unsupervised feature selection. Pattern Recognition, 2016, 53: 87\u2013101","journal-title":"Pattern Recognition"},{"key":"3579_CR32","doi-asserted-by":"publisher","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, et al. Unsupervised feature selection by regularized self-representation. Pattern Recognition, 2015, 48: 438\u2013446","journal-title":"Pattern Recognition"},{"key":"3579_CR33","doi-asserted-by":"crossref","unstructured":"Wang S, Tang J, Liu H. Embedded unsupervised feature selection. In: Proceedings of AAAI Conference on Artificial Intelligence, 2015. 470\u2013476","DOI":"10.1609\/aaai.v29i1.9211"},{"key":"3579_CR34","doi-asserted-by":"publisher","first-page":"3941","DOI":"10.1109\/TCYB.2016.2591068","volume":"47","author":"L Zhu","year":"2016","unstructured":"Zhu L, Shen J, Xie L, et al. Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE Trans Cybern, 2016, 47: 3941\u20133954","journal-title":"IEEE Trans Cybern"},{"key":"3579_CR35","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1109\/TCYB.2018.2881539","volume":"50","author":"Y Han","year":"2018","unstructured":"Han Y, Zhu L, Cheng Z, et al. Discrete optimal graph clustering. IEEE Trans Cybern, 2018, 50: 1697\u20131710","journal-title":"IEEE Trans Cybern"},{"key":"3579_CR36","doi-asserted-by":"publisher","first-page":"4424","DOI":"10.1109\/TNNLS.2019.2955209","volume":"31","author":"D Shi","year":"2019","unstructured":"Shi D, Zhu L, Li Y, et al. Robust structured graph clustering. IEEE Trans Neural Netw Learn Syst, 2019, 31: 4424\u20134436","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR37","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/TIP.2018.2870936","volume":"28","author":"J Jiang","year":"2018","unstructured":"Jiang J, Yu Y, Wang Z, et al. Graph-regularized locality-constrained joint dictionary and residual learning for face sketch synthesis. IEEE Trans Image Process, 2018, 28: 628\u2013641","journal-title":"IEEE Trans Image Process"},{"key":"3579_CR38","doi-asserted-by":"publisher","first-page":"107375","DOI":"10.1016\/j.patcog.2020.107375","volume":"105","author":"P Zhou","year":"2020","unstructured":"Zhou P, Du L, Li X, et al. Unsupervised feature selection with adaptive multiple graph learning. Pattern Recognition, 2020, 105: 107375","journal-title":"Pattern Recognition"},{"key":"3579_CR39","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TNNLS.2020.3029523","volume":"33","author":"J Jiang","year":"2022","unstructured":"Jiang J, Ma J, Liu X. Multilayer spectral-spatial graphs for label noisy robust hyperspectral image classification. IEEE Trans Neural Netw Learn Syst, 2022, 33: 839\u2013852","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR40","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.inffus.2020.12.007","volume":"70","author":"H Zhang","year":"2021","unstructured":"Zhang H, Wu D, Nie F, et al. Multilevel projections with adaptive neighbor graph for unsupervised multi-view feature selection. Inf Fusion, 2021, 70: 129\u2013140","journal-title":"Inf Fusion"},{"key":"3579_CR41","doi-asserted-by":"publisher","first-page":"4705","DOI":"10.1109\/TKDE.2020.3048678","volume":"34","author":"C Tang","year":"2022","unstructured":"Tang C, Zheng X, Liu X, et al. Cross-view locality preserved diversity and consensus learning for multi-view unsupervised feature selection. IEEE Trans Knowl Data Eng, 2022, 34: 4705\u20134716","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3579_CR42","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1109\/TKDE.2011.222","volume":"25","author":"Z Zhao","year":"2011","unstructured":"Zhao Z, Wang L, Liu H, et al. On similarity preserving feature selection. IEEE Trans Knowl Data Eng, 2011, 25: 619\u2013632","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3579_CR43","doi-asserted-by":"publisher","first-page":"2855","DOI":"10.1016\/j.neucom.2017.11.061","volume":"275","author":"P Zhu","year":"2018","unstructured":"Zhu P, Xu Q, Hu Q, et al. Co-regularized unsupervised feature selection. Neurocomputing, 2018, 275: 2855\u20132863","journal-title":"Neurocomputing"},{"key":"3579_CR44","doi-asserted-by":"publisher","first-page":"105102","DOI":"10.1016\/j.knosys.2019.105102","volume":"189","author":"Z Kang","year":"2020","unstructured":"Kang Z, Shi G, Huang S, et al. Multi-graph fusion for multi-view spectral clustering. Knowledge-Based Syst, 2020, 189: 105102","journal-title":"Knowledge-Based Syst"},{"key":"3579_CR45","doi-asserted-by":"publisher","first-page":"102057","DOI":"10.1016\/j.media.2021.102057","volume":"71","author":"J Gan","year":"2021","unstructured":"Gan J, Peng Z, Zhu X, et al. Brain functional connectivity analysis based on multi-graph fusion. Med Image Anal, 2021, 71: 102057","journal-title":"Med Image Anal"},{"key":"3579_CR46","doi-asserted-by":"publisher","first-page":"7789","DOI":"10.1609\/aaai.v35i9.16951","volume":"35","author":"R Hu","year":"2021","unstructured":"Hu R, Deng Z, Zhu X. Multi-scale graph fusion for co-saliency detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. 35: 7789\u20137796","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3579_CR47","doi-asserted-by":"crossref","unstructured":"Du L, Shen Y D. Unsupervised feature selection with adaptive structure learning. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. 209\u2013218","DOI":"10.1145\/2783258.2783345"},{"key":"3579_CR48","doi-asserted-by":"crossref","unstructured":"Nie F, Zhu W, Li X. Unsupervised feature selection with structured graph optimization. In: Proceedings of AAAI Conference on Artificial Intelligence, 2016","DOI":"10.1609\/aaai.v30i1.10168"},{"key":"3579_CR49","doi-asserted-by":"crossref","unstructured":"Fan M, Chang X, Tao D. Structure regularized unsupervised discriminant feature analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2017","DOI":"10.1609\/aaai.v31i1.10789"},{"key":"3579_CR50","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.patcog.2017.01.016","volume":"66","author":"P Zhu","year":"2017","unstructured":"Zhu P, Zhu W, Hu Q, et al. Subspace clustering guided unsupervised feature selection. Pattern Recognition, 2017, 66: 364\u2013374","journal-title":"Pattern Recognition"},{"key":"3579_CR51","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1109\/TNNLS.2017.2650978","volume":"29","author":"M Luo","year":"2017","unstructured":"Luo M, Nie F, Chang X, et al. Adaptive unsupervised feature selection with structure regularization. IEEE Trans Neural Netw Learn Syst, 2017, 29: 944\u2013956","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR52","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. Generalized uncorrelated regression with adaptive graph for unsupervised feature selection. IEEE Trans Neural Netw Learn Syst, 2018, 30: 1587\u20131595","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR53","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1109\/TNNLS.2019.2920905","volume":"31","author":"R Zhou","year":"2019","unstructured":"Zhou R, Chang X, Shi L, et al. Person reidentification via multi-feature fusion with adaptive graph learning. IEEE Trans Neural Netw Learn Syst, 2019, 31: 1592\u20131601","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR54","doi-asserted-by":"publisher","first-page":"6323","DOI":"10.1109\/TNNLS.2018.2829867","volume":"29","author":"Z Li","year":"2018","unstructured":"Li Z, Nie F, Chang X, et al. Dynamic affinity graph construction for spectral clustering using multiple features. IEEE Trans Neural Netw Learn Syst, 2018, 29: 6323\u20136332","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3579_CR55","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.patcog.2016.06.009","volume":"63","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Bai L, Liang Y, et al. Joint hypergraph learning and sparse regression for feature selection. Pattern Recognition, 2017, 63: 291\u2013309","journal-title":"Pattern Recognition"},{"key":"3579_CR56","doi-asserted-by":"crossref","unstructured":"Zhu X, Zhu Y, Zhang S, et al. Adaptive hypergraph learning for unsupervised feature selection. In: Proceedings of International Joint Conference on Artificial Intelligence, 2017. 3581\u20133587","DOI":"10.24963\/ijcai.2017\/501"},{"key":"3579_CR57","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. Unsupervised feature selection via adaptive hypergraph regularized latent representation learning. Neurocomputing, 2020, 378: 79\u201397","journal-title":"Neurocomputing"},{"key":"3579_CR58","doi-asserted-by":"publisher","first-page":"105512","DOI":"10.1016\/j.knosys.2020.105512","volume":"193","author":"G Zhong","year":"2020","unstructured":"Zhong G, Pun C M. Subspace clustering by simultaneously feature selection and similarity learning. Knowledge-Based Syst, 2020, 193: 105512","journal-title":"Knowledge-Based Syst"},{"key":"3579_CR59","doi-asserted-by":"publisher","first-page":"5522","DOI":"10.1109\/TCYB.2020.3034462","volume":"52","author":"A Yuan","year":"2022","unstructured":"Yuan A, You M, He D, et al. Convex non-negative matrix factorization with adaptive graph for unsupervised feature selection. IEEE Trans Cybern, 2022, 52: 5522\u20135534","journal-title":"IEEE Trans Cybern"},{"key":"3579_CR60","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"S Boyd","year":"2004","unstructured":"Boyd S, Vandenberghe L. Convex Optimization. Cambridge: Cambridge University Press, 2004"},{"key":"3579_CR61","first-page":"1","volume":"50","author":"J Li","year":"2017","unstructured":"Li J, Cheng K, Wang S, et al. Feature selection: a data perspective. ACM Comput Surveys, 2017, 50: 1\u201345","journal-title":"ACM Comput Surveys"},{"key":"3579_CR62","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1109\/TPAMI.2005.92","volume":"27","author":"K-C Lee","year":"2005","unstructured":"Lee K-C, Ho J, Kriegman D J. Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Machine Intell, 2005, 27: 684\u2013698","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"3579_CR63","unstructured":"Samaria F S, Harter A C. Parameterisation of a stochastic model for human face identification. In: Proceedings of IEEE Workshop on Applications of Computer Vision, 1994. 138\u2013142"},{"key":"3579_CR64","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/0031-3203(91)90074-F","volume":"24","author":"Z Q Hong","year":"1991","unstructured":"Hong Z Q, Yang J Y. Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition, 1991, 24: 317\u2013324","journal-title":"Pattern Recognition"},{"key":"3579_CR65","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1016\/j.sigpro.2014.12.027","volume":"120","author":"X Du","year":"2016","unstructured":"Du X, Yan Y, Pan P, et al. Multiple graph unsupervised feature selection. Signal Processing, 2016, 120: 754\u2013760","journal-title":"Signal Processing"},{"key":"3579_CR66","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.neucom.2018.06.010","volume":"314","author":"W Zheng","year":"2018","unstructured":"Zheng W, Xu C, Yang J, et al. Low-rank structure preserving for unsupervised feature selection. Neurocomputing, 2018, 314: 360\u2013370","journal-title":"Neurocomputing"},{"key":"3579_CR67","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.knosys.2018.01.009","volume":"145","author":"C Tang","year":"2018","unstructured":"Tang C, Liu X, Li M, et al. Robust unsupervised feature selection via dual self-representation and manifold regularization. Knowledge-Based Syst, 2018, 145: 109\u2013120","journal-title":"Knowledge-Based Syst"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3579-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-022-3579-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3579-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T21:03:59Z","timestamp":1718744639000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-022-3579-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":67,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["3579"],"URL":"https:\/\/doi.org\/10.1007\/s11432-022-3579-1","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,19]]},"assertion":[{"value":"16 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"152101"}}