{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T09:11:56Z","timestamp":1770541916583,"version":"3.49.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671633"],"award-info":[{"award-number":["81671633"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Stat Comput"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11222-024-10507-4","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T10:02:07Z","timestamp":1728295327000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive sufficient sparse clustering by controlling false discovery"],"prefix":"10.1007","volume":"34","author":[{"given":"Zihao","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houxiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangxin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"issue":"3","key":"10507_CR1","first-page":"355","volume":"62","author":"N Adeen","year":"2020","unstructured":"Adeen, N., Abdulazeez, M., Zeebaree, D.: Systematic review of unsupervised genomic clustering algorithms techniques for high dimensional datasets. Technol. Rep. Kansai Univ. 62(3), 355\u2013374 (2020)","journal-title":"Technol. Rep. Kansai Univ."},{"issue":"1","key":"10507_CR2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/10618600.2017.1305278","volume":"27","author":"S Amiri","year":"2018","unstructured":"Amiri, S., Clarke, B.S., Clarke, J.L.: Clustering categorical data via ensembling dissimilarity matrices. J. Comput. Graph. Stat. 27(1), 195\u2013208 (2018)","journal-title":"J. Comput. Graph. Stat."},{"issue":"9","key":"10507_CR3","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01605682.2017.1398206","volume":"69","author":"S Benati","year":"2018","unstructured":"Benati, S., Garc\u00eda, S., Puerto, J.: Mixed integer linear programming and heuristic methods for feature selection in clustering. J. Operat. Res. Soc. 69(9), 1379\u20131395 (2018)","journal-title":"J. Operat. Res. Soc."},{"issue":"1","key":"10507_CR4","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57(1), 289\u2013300 (1995)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"10507_CR5","unstructured":"Bishop, C.: Pattern recognition and machine learning. 16, 140\u2013155 (2006)"},{"issue":"2","key":"10507_CR6","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/ejhg.2012.129","volume":"21","author":"V Costa","year":"2013","unstructured":"Costa, V., Aprile, M., Esposito, R., Ciccodicola, A.: RNA-SEQ and human complex diseases: recent accomplishments and future perspectives. Eur. J. Hum. Genet. 21(2), 134\u2013142 (2013)","journal-title":"Eur. J. Hum. Genet."},{"issue":"510","key":"10507_CR7","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1080\/01621459.2014.920256","volume":"110","author":"H Cui","year":"2015","unstructured":"Cui, H., Li, R., Zhong, W.: Model-free feature screening for ultrahigh dimensional discriminant analysis. J. Am. Stat. Assoc. 110(510), 630\u2013641 (2015)","journal-title":"J. Am. Stat. Assoc."},{"key":"10507_CR8","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1093\/biostatistics\/kxj007","volume":"7","author":"HA Chipman","year":"2005","unstructured":"Chipman, H.A., Tibshirani, R.: Hybrid hierarchical clustering with applications to microarray data. Biostatistics 7, 286\u2013301 (2005)","journal-title":"Biostatistics"},{"issue":"4","key":"10507_CR9","doi-asserted-by":"crossref","first-page":"2123","DOI":"10.1214\/13-AOS1139","volume":"41","author":"J Chang","year":"2013","unstructured":"Chang, J., Tang, C.Y., Wu, Y.: Marginal empirical likelihood and sure independence feature screening. Ann. Stat. 41(4), 2123\u20132148 (2013)","journal-title":"Ann. Stat."},{"issue":"3","key":"10507_CR10","first-page":"1265","volume":"28","author":"X Chang","year":"2018","unstructured":"Chang, X., Wang, Y., Li, R., Xu, Z.: Sparse k-means with $$\\ell _\\infty $$\/$$\\ell _0$$ penalty for high-dimensional data clustering. Stat. Sin. 28(3), 1265\u20131284 (2018)","journal-title":"Stat. Sin."},{"issue":"4","key":"10507_CR11","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1007\/s00454-005-1220-0","volume":"35","author":"DL Donoho","year":"2006","unstructured":"Donoho, D.L.: High-dimensional centrally symmetric polytopes with neighborliness proportional to dimension. Discrete Comput. Geomet. 35(4), 617\u2013652 (2006)","journal-title":"Discrete Comput. Geomet."},{"issue":"494","key":"10507_CR12","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1198\/jasa.2011.tm09779","volume":"106","author":"J Fan","year":"2011","unstructured":"Fan, J., Feng, Y., Song, R.: Nonparametric independence screening in sparse ultra-high-dimensional additive models. J. Am. Stat. Assoc. 106(494), 544\u2013557 (2011)","journal-title":"J. Am. Stat. Assoc."},{"issue":"5","key":"10507_CR13","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","volume":"70","author":"J Fan","year":"2008","unstructured":"Fan, J., Lv, J.: Sure independence screening for ultrahigh dimensional feature space. J. R. Stat. Soc. B Stat. Methodol. 70(5), 849\u2013883 (2008)","journal-title":"J. R. Stat. Soc. B Stat. Methodol."},{"issue":"6","key":"10507_CR14","first-page":"3567","volume":"38","author":"J Fan","year":"2010","unstructured":"Fan, J., Song, R.: Sure independence screening in generalized linear models with np-dimensionality. Ann. Stat. 38(6), 3567\u20133604 (2010)","journal-title":"Ann. Stat."},{"issue":"3","key":"10507_CR15","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1111\/j.1541-0420.2009.01341.x","volume":"66","author":"J Guo","year":"2010","unstructured":"Guo, J., Levina, E., Michailidis, G., Zhu, J.: Pairwise variable selection for high-dimensional model-based clustering. Biometrics 66(3), 793\u2013804 (2010)","journal-title":"Biometrics"},{"key":"10507_CR16","unstructured":"Guo, X., Ren, H., Zou, C., Li, R.: Threshold selection in feature screening for error rate control. J. Am. Stat. Assoc. 1\u201313 (2022)"},{"issue":"4","key":"10507_CR17","first-page":"1995","volume":"47","author":"X Han","year":"2019","unstructured":"Han, X.: Nonparametric screening under conditional strictly convex loss for ultrahigh dimensional sparse data. Ann. Stat. 47(4), 1995\u20132022 (2019)","journal-title":"Ann. Stat."},{"key":"10507_CR18","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: Data mining, inference, and prediction 2nd edition. (2020)"},{"issue":"4","key":"10507_CR19","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1080\/10705511.2017.1402334","volume":"25","author":"MN Hallquist","year":"2018","unstructured":"Hallquist, M.N., Wiley, J.F.: Mplusautomation: An r package for facilitating large-scale latent variable analyses in mplus. Struct. Equ. Model. 25(4), 621\u2013638 (2018)","journal-title":"Struct. Equ. Model."},{"issue":"1","key":"10507_CR20","first-page":"342","volume":"41","author":"X He","year":"2013","unstructured":"He, X., Wang, L., Hong, H.G.: Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data. Ann. Stat. 41(1), 342\u2013369 (2013)","journal-title":"Ann. Stat."},{"issue":"4","key":"10507_CR21","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/00031305.2016.1264311","volume":"71","author":"N Hao","year":"2017","unstructured":"Hao, N., Zhang, H.H.: A note on high-dimensional linear regression with interactions. Am. Stat. 71(4), 291\u2013297 (2017)","journal-title":"Am. Stat."},{"key":"10507_CR22","unstructured":"Lin, X., Guan, J., Chen, B., Zeng, Y.: Unsupervised feature selection via orthogonal basis clustering and local structure preserving. IEEE Trans. Neural Netw. Learn. Syst. 1\u201312 (2021)"},{"issue":"537","key":"10507_CR23","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1080\/01621459.2020.1783274","volume":"117","author":"W Liu","year":"2022","unstructured":"Liu, W., Ke, Y., Liu, J., Li, R.: Model-free feature screening and FDR control with knockoff features. J. Am. Stat. Assoc. 117(537), 428\u2013443 (2022)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"10507_CR24","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1080\/10618600.2012.679226","volume":"21","author":"H Lee","year":"2012","unstructured":"Lee, H., Li, J.: Variable selection for clustering by separability based on ridgelines. J. Comput. Graph. Stat. 21(2), 315\u2013336 (2012)","journal-title":"J. Comput. Graph. Stat."},{"key":"10507_CR25","doi-asserted-by":"crossref","unstructured":"Liu, W., Li, R.: In Fuleky, P. (ed.) Variable Selection and Feature Screening, pp. 293\u2013326. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-31150-6_10"},{"issue":"1","key":"10507_CR26","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00362-017-0931-7","volume":"61","author":"J Lu","year":"2020","unstructured":"Lu, J., Lin, L.: Model-free conditional screening via conditional distance correlation. Stat. Pap. 61(1), 225\u2013244 (2020)","journal-title":"Stat. Pap."},{"issue":"3","key":"10507_CR27","first-page":"1846","volume":"40","author":"G Li","year":"2012","unstructured":"Li, G., Peng, H., Zhang, J., Zhu, L.: Robust rank correlation based screening. Ann. Stat. 40(3), 1846\u20131877 (2012)","journal-title":"Ann. Stat."},{"issue":"1","key":"10507_CR28","first-page":"481","volume":"15","author":"DK Lim","year":"2021","unstructured":"Lim, D.K., Rashid, N.U., Ibrahim, J.G.: Model-based feature selection and clustering of RNA-SEQ data for unsupervised subtype discovery. Annals Appl. Stat. 15(1), 481\u2013508 (2021)","journal-title":"Annals Appl. Stat."},{"key":"10507_CR29","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.csda.2013.05.016","volume":"67","author":"L Lin","year":"2013","unstructured":"Lin, L., Sun, J., Zhu, L.: Nonparametric feature screening. Comput. Stat. Data Anal. 67, 162\u2013174 (2013)","journal-title":"Comput. Stat. Data Anal."},{"issue":"12","key":"10507_CR30","doi-asserted-by":"crossref","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. 24(12), 5343\u20135355 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"499","key":"10507_CR31","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1080\/01621459.2012.695654","volume":"107","author":"R Li","year":"2012","unstructured":"Li, R., Zhong, W., Zhu, L.: Feature screening via distance correlation learning. J. Am. Stat. Assoc. 107(499), 1129\u20131139 (2012)","journal-title":"J. Am. Stat. Assoc."},{"key":"10507_CR32","unstructured":"MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281\u2013297 (1967). Oakland"},{"key":"10507_CR33","doi-asserted-by":"crossref","unstructured":"Mohamed, I.B., Mirakhmedov, S.M.: Approximation by normal distribution for a sample sum in sampling without replacement from a finite population. Sankhya A 78, 188\u2013220 (2016)","DOI":"10.1007\/s13171-016-0088-9"},{"key":"10507_CR34","doi-asserted-by":"crossref","unstructured":"Maji, P., Pal, S.K.: Fuzzy-rough feature selection using -information measures, pp. 117\u2013159 (2012)","DOI":"10.1002\/9781118119723.ch5"},{"key":"10507_CR35","volume-title":"Convergence of Stochastic Processes","author":"D Pollard","year":"2012","unstructured":"Pollard, D.: Convergence of Stochastic Processes. Springer, Berlin (2012)"},{"issue":"5","key":"10507_CR36","doi-asserted-by":"crossref","first-page":"2301099","DOI":"10.1002\/adts.202301099","volume":"7","author":"H Qiu","year":"2024","unstructured":"Qiu, H., Chen, J., Yuan, Z.: Quantile correlation-based sufficient variable screening by controlling false discovery rate. Adv. Theory Simul. 7(5), 2301099 (2024)","journal-title":"Adv. Theory Simul."},{"issue":"30","key":"10507_CR37","doi-asserted-by":"crossref","first-page":"978","DOI":"10.21105\/joss.00978","volume":"3","author":"JM Rosenberg","year":"2018","unstructured":"Rosenberg, J.M., Beymer, P.N., Anderson, D.J., Lissa, Cj., Schmidt, J.A.: tidylpa: an r package to easily carry out latent profile analysis (LPA) using open-source or commercial software. J. Open Sour. Softw. 3(30), 978 (2018)","journal-title":"J. Open Sour. Softw."},{"issue":"1","key":"10507_CR38","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/10618600.2021.1982724","volume":"31","author":"B Seo","year":"2022","unstructured":"Seo, B., Lin, L., Li, J.: Block-wise variable selection for clustering via latent states of mixture models. J. Comput. Graph. Stat. 31(1), 138\u2013150 (2022)","journal-title":"J. Comput. Graph. Stat."},{"key":"10507_CR39","doi-asserted-by":"crossref","unstructured":"Shao, S., Tunc, C., Al-Shawi, A., Hariri, S.: Automated twitter author clustering with unsupervised learning for social media forensics. In: 2019 IEEE\/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pp. 1\u20138 (2019)","DOI":"10.1109\/AICCSA47632.2019.9035286"},{"issue":"10","key":"10507_CR40","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1109\/TNNLS.2013.2263427","volume":"24","author":"M Tan","year":"2013","unstructured":"Tan, M., Tsang, I.W., Wang, L.: Minimax sparse logistic regression for very high-dimensional feature selection. IEEE Trans. Neural Netw. Learn. Syst. 24(10), 1609\u20131622 (2013)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10507_CR41","unstructured":"Tang, W., Xie, J., Lin, Y., Tang, N.: Quantile correlation-based variable selection. J. Bus. Econ. Stat. 1\u201313 (2021)"},{"issue":"4","key":"10507_CR42","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Von Luxburg","year":"2007","unstructured":"Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"issue":"521","key":"10507_CR43","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01621459.2017.1330202","volume":"113","author":"ML Wallace","year":"2018","unstructured":"Wallace, M.L., Buysse, D.J., Germain, A., Hall, M.H., Iyengar, S.: Variable selection for skewed model-based clustering: Application to the identification of novel sleep phenotypes. J. Am. Stat. Assoc. 113(521), 95\u2013110 (2018)","journal-title":"J. Am. Stat. Assoc."},{"key":"10507_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chang, X., Li, R., Xu, Z.: Sparse k-means with the $$\\ell _q(0 \\le q < 1)$$ constraint for high-dimensional data clustering. In: 2013 IEEE 13th International Conference on Data Mining, pp. 797\u2013806 (2013)","DOI":"10.1109\/ICDM.2013.64"},{"key":"10507_CR45","first-page":"019","volume":"2015","author":"Q Wan","year":"2015","unstructured":"Wan, Q., Dingerdissen, H., Fan, Y., Gulzar, N., Pan, Y., Wu, T.-J., Yan, C., Zhang, H., Mazumder, R.: Bioxpress: an integrated RNA-SEQ-derived gene expression database for pan-cancer analysis. Database 2015, 019 (2015)","journal-title":"Database"},{"key":"10507_CR46","doi-asserted-by":"crossref","unstructured":"Wang, H., Pang, G., Shen, C., Ma, C.: Unsupervised representation learning by predicting random distances. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 2950\u20132956 (2021)","DOI":"10.24963\/ijcai.2020\/408"},{"key":"10507_CR47","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1198\/jasa.2010.tm09415","volume":"105","author":"D Witten","year":"2010","unstructured":"Witten, D., Tibshirani, R.: A framework for feature selection in clustering. J. Am. Stat. Assoc. 105, 713\u2013726 (2010)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"10507_CR48","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1080\/10618600.2017.1377081","volume":"27","author":"B Wang","year":"2018","unstructured":"Wang, B., Zhang, Y., Sun, W.W., Fang, Y.: Sparse convex clustering. J. Comput. Graph. Stat. 27(2), 393\u2013403 (2018)","journal-title":"J. Comput. Graph. Stat."},{"issue":"530","key":"10507_CR49","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1080\/01621459.2019.1573734","volume":"115","author":"J Xie","year":"2020","unstructured":"Xie, J., Lin, Y., Yan, X., Tang, N.: Category-adaptive variable screening for ultra-high dimensional heterogeneous categorical data. J. Am. Stat. Assoc. 115(530), 747\u2013760 (2020)","journal-title":"J. Am. Stat. Assoc."},{"key":"10507_CR50","volume":"138","author":"M-S Yang","year":"2023","unstructured":"Yang, M.-S., Benjamin, J.B.M.: Sparse possibilistic c-means clustering with lasso. Pattern Recogn. 138, 109348 (2023)","journal-title":"Pattern Recogn."},{"key":"10507_CR51","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2022.107530","volume":"174","author":"Q Yuan","year":"2022","unstructured":"Yuan, Q., Chen, X., Ke, C., Yin, X.: Independence index sufficient variable screening for categorical responses. Comput. Stat. Data Anal. 174, 107530 (2022)","journal-title":"Comput. Stat. Data Anal."},{"issue":"3","key":"10507_CR52","doi-asserted-by":"crossref","first-page":"524","DOI":"10.3390\/e25030524","volume":"25","author":"Z Yuan","year":"2023","unstructured":"Yuan, Z., Chen, J., Qiu, H., Huang, Y.: Quantile-adaptive sufficient variable screening by controlling false discovery. Entropy 25(3), 524 (2023)","journal-title":"Entropy"},{"issue":"4","key":"10507_CR53","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1111\/rssb.12093","volume":"77","author":"X Yin","year":"2015","unstructured":"Yin, X., Hilafu, H.: Sequential sufficient dimension reduction for large $$p$$, small $$n$$ problems. J. R. Stat. Soc. B (Stat. Methodol.) 77(4), 879\u2013892 (2015)","journal-title":"J. R. Stat. Soc. B (Stat. Methodol.)"},{"issue":"11","key":"10507_CR54","doi-asserted-by":"crossref","first-page":"5257","DOI":"10.1109\/TIP.2017.2733200","volume":"26","author":"C Yao","year":"2017","unstructured":"Yao, C., Liu, Y.-F., Jiang, B., Han, J., Han, J.: Lle score: a new filter-based unsupervised feature selection method based on nonlinear manifold embedding and its application to image recognition. IEEE Trans. Image Process. 26(11), 5257\u20135269 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"10507_CR55","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/biom.13664","volume":"79","author":"H Yu","year":"2023","unstructured":"Yu, H., Wang, Y., Zeng, D.: A general framework of nonparametric feature selection in high-dimensional data. Biometrics 79(2), 951\u2013963 (2023)","journal-title":"Biometrics"},{"issue":"496","key":"10507_CR56","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1198\/jasa.2011.tm10563","volume":"106","author":"L-P Zhu","year":"2011","unstructured":"Zhu, L.-P., Li, L., Li, R., Zhu, L.-X.: Model-free feature screening for ultrahigh-dimensional data. J. Am. Stat. Assoc. 106(496), 1464\u20131475 (2011)","journal-title":"J. Am. Stat. Assoc."},{"issue":"10","key":"10507_CR57","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/TCYB.2018.2846404","volume":"49","author":"Y Zhao","year":"2018","unstructured":"Zhao, Y., Shrivastava, A.K., Tsui, K.L.: Regularized gaussian mixture model for high-dimensional clustering. IEEE Trans. Cyber. 49(10), 3677\u20133688 (2018)","journal-title":"IEEE Trans. Cyber."},{"issue":"531","key":"10507_CR58","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1080\/01621459.2019.1632078","volume":"115","author":"T Zhou","year":"2020","unstructured":"Zhou, T., Zhu, L., Xu, C., Li, R.: Model-free forward screening via cumulative divergence. J. Am. Stat. Assoc. 115(531), 1393\u20131405 (2020)","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Statistics and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-024-10507-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11222-024-10507-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-024-10507-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T12:12:33Z","timestamp":1732536753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11222-024-10507-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,7]]},"references-count":58,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10507"],"URL":"https:\/\/doi.org\/10.1007\/s11222-024-10507-4","relation":{},"ISSN":["0960-3174","1573-1375"],"issn-type":[{"value":"0960-3174","type":"print"},{"value":"1573-1375","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,7]]},"assertion":[{"value":"20 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","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 declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"193"}}