{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T01:49:51Z","timestamp":1743904191338,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"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":["11571074"],"award-info":[{"award-number":["11571074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Fujian Province, China","award":["2022J01102"],"award-info":[{"award-number":["2022J01102"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04284-8","type":"journal-article","created":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T12:03:02Z","timestamp":1668859382000},"page":"15476-15495","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Homogeneous ensemble extreme learning machine autoencoder with mutual representation learning and manifold regularization for medical datasets"],"prefix":"10.1007","volume":"53","author":[{"given":"Wenjian","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3178-1949","authenticated-orcid":false,"given":"Xiaoyun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanming","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"4284_CR1","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, Wang P, Chen J, Wang L, Li W (2018) Robust unsupervised feature selection via dual self-representation and manifold regularization. Knowl Based Syst 145:109\u2013120. https:\/\/doi.org\/10.1016\/j.knosys.2018.01.009","journal-title":"Knowl Based Syst"},{"key":"4284_CR2","doi-asserted-by":"publisher","unstructured":"Luo T, Yang Y, Yi D, Ye J (2017) Robust discriminative feature learning with calibrated data reconstruction and sparse low-rank model. Appl Intell:1\u201314. https:\/\/doi.org\/10.1007\/s10489-017-1060-7","DOI":"10.1007\/s10489-017-1060-7"},{"issue":"11","key":"4284_CR3","doi-asserted-by":"publisher","first-page":"6581","DOI":"10.1007\/s00521-019-04117-9","volume":"32","author":"T Chen","year":"2020","unstructured":"Chen T, Guo Y, Hao S (2020) Unsupervised feature selection based on joint spectral learning and general sparse regression. Neural Comput Appl 32(11):6581\u20136589. https:\/\/doi.org\/10.1007\/s00521-019-04117-9","journal-title":"Neural Comput Appl"},{"issue":"12","key":"4284_CR4","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1109\/TKDE.2018.2877746","volume":"31","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Zhang Z, Li S, Qin J, Liu G, Wang M, Yan S (2019) Unsupervised nonnegative adaptive feature extraction for data representation. IEEE Trans Knowl Data Eng 31(12):2423\u20132440. https:\/\/doi.org\/10.1109\/TKDE.2018.2877746","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"4284_CR5","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/s10489-019-01526-0","volume":"50","author":"M Koohzadi","year":"2020","unstructured":"Koohzadi M, Charkari NM, Ghaderi F (2020) Unsupervised representation learning based on the deep multi-view ensemble learning. Appl Intell 50(2):562\u2013581. https:\/\/doi.org\/10.1007\/s10489-019-01526-0","journal-title":"Appl Intell"},{"key":"4284_CR6","doi-asserted-by":"publisher","first-page":"107758","DOI":"10.1016\/j.patcog.2020.107758","volume":"113","author":"J Lu","year":"2021","unstructured":"Lu J, Wang H, Zhou J, Chen Y, Lai Z, Hu Q (2021) Low-rank adaptive graph embedding for unsupervised feature extraction. Pattern Recognit 113:107758. https:\/\/doi.org\/10.1016\/j.patcog.2020.107758","journal-title":"Pattern Recognit"},{"issue":"2","key":"4284_CR7","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1109\/TKDE.2020.2983396","volume":"34","author":"R Wang","year":"2022","unstructured":"Wang R, Bian J, Nie F, Li X (2022) Unsupervised discriminative projection for feature selection. IEEE Trans Knowl Data Eng 34(2):942\u2013953. https:\/\/doi.org\/10.1109\/TKDE.2020.2983396","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"4284_CR8","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1109\/TNNLS.2017.2692773","volume":"29","author":"D Chen","year":"2018","unstructured":"Chen D, Lv J, Yi Z (2018) Graph regularized restricted boltzmann machine. IEEE Trans Neural Netw Learn Syst 29(6):2651\u20132659. https:\/\/doi.org\/10.1109\/TNNLS.2017.2692773","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4284_CR9","unstructured":"Zhang N, Sun S (2021) Multiview graph restricted boltzmann machines. IEEE Trans Cybern:1\u201315"},{"key":"4284_CR10","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.neucom.2018.05.117","volume":"312","author":"S Feng","year":"2018","unstructured":"Feng S, Duarte MF (2018) Graph autoencoder-based unsupervised feature selection with broad and local data structure preservation. Neurocomputing 312:310\u2013323. https:\/\/doi.org\/10.1016\/j.neucom.2018.05.117","journal-title":"Neurocomputing"},{"issue":"9","key":"4284_CR11","doi-asserted-by":"publisher","first-page":"12061","DOI":"10.1007\/s11042-020-10474-8","volume":"81","author":"X Wang","year":"2022","unstructured":"Wang X, Wang Z, Zhang Y, Jiang X, Cai Z (2022) Latent representation learning based autoencoder for unsupervised feature selection in hyperspectral imagery. Multim Tools Appl 81(9):12061\u201312075. https:\/\/doi.org\/10.1007\/s11042-020-10474-8","journal-title":"Multim Tools Appl"},{"key":"4284_CR12","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neunet.2022.03.004","volume":"150","author":"X Gong","year":"2022","unstructured":"Gong X, Yu L, Wang J, Zhang K, Bai X, Pal NR (2022) Unsupervised feature selection via adaptive autoencoder with redundancy control. Neural Netw 150:87\u2013101. https:\/\/doi.org\/10.1016\/j.neunet.2022.03.004","journal-title":"Neural Netw"},{"key":"4284_CR13","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.neucom.2019.05.050","volume":"358","author":"J Yu","year":"2019","unstructured":"Yu J (2019) Manifold regularized stacked denoising autoencoders with feature selection. Neurocomputing 358:235\u2013245. https:\/\/doi.org\/10.1016\/j.neucom.2019.05.050","journal-title":"Neurocomputing"},{"issue":"10","key":"4284_CR14","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1177\/0142331219898937","volume":"42","author":"Y Hui","year":"2020","unstructured":"Hui Y, Zhao X (2020) Sparse representation preserving embedding based on extreme learning machine for process monitoring. Trans Inst Meas Control 42(10):1895\u20131907. https:\/\/doi.org\/10.1177\/0142331219898937","journal-title":"Trans Inst Meas Control"},{"key":"4284_CR15","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neucom.2017.01.115","volume":"277","author":"T Liu","year":"2018","unstructured":"Liu T, Kasun LLC, Huang G, Lin Z (2018) Extreme learning machine for joint embedding and clustering. Neurocomputing 277:78\u201388. https:\/\/doi.org\/10.1016\/j.neucom.2017.01.115","journal-title":"Neurocomputing"},{"key":"4284_CR16","doi-asserted-by":"publisher","first-page":"48884","DOI":"10.1109\/ACCESS.2021.3068959","volume":"9","author":"L Shao","year":"2021","unstructured":"Shao L, Kang R, Yi W, Zhang H (2021) An enhanced unsupervised extreme learning machine based method for the nonlinear fault detection. IEEE Access 9:48884\u201348898. https:\/\/doi.org\/10.1109\/ACCESS.2021.3068959","journal-title":"IEEE Access"},{"key":"4284_CR17","doi-asserted-by":"publisher","first-page":"106053","DOI":"10.1109\/ACCESS.2019.2932017","volume":"7","author":"H Zhang","year":"2019","unstructured":"Zhang H, Deng X, Zhang Y, Hou C, Li C, Xin Z (2019) Nonlinear process monitoring based on global preserving unsupervised kernel extreme learning machine. IEEE Access 7:106053\u2013106064. https:\/\/doi.org\/10.1109\/ACCESS.2019.2932017","journal-title":"IEEE Access"},{"key":"4284_CR18","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.neucom.2019.12.065","volume":"386","author":"J Chen","year":"2020","unstructured":"Chen J, Zeng Y, Li Y, Huang G (2020) Unsupervised feature selection based extreme learning machine for clustering. Neurocomputing 386:208\u2013220. https:\/\/doi.org\/10.1016\/j.neucom.2019.12.065","journal-title":"Neurocomputing"},{"issue":"8","key":"4284_CR19","doi-asserted-by":"publisher","first-page":"3906","DOI":"10.1109\/TIP.2016.2570569","volume":"25","author":"LLC Kasun","year":"2016","unstructured":"Kasun LLC, Yang Y, Huang G, Zhang Z (2016) Dimension reduction with extreme learning machine. IEEE Trans Image Process 25(8):3906\u20133918. https:\/\/doi.org\/10.1109\/TIP.2016.2570569","journal-title":"IEEE Trans Image Process"},{"key":"4284_CR20","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.neucom.2016.12.027","volume":"230","author":"K Sun","year":"2017","unstructured":"Sun K, Zhang J, Zhang C, Hu J (2017) Generalized extreme learning machine autoencoder and a new deep neural network. Neurocomputing 230:374\u2013381. https:\/\/doi.org\/10.1016\/j.neucom.2016.12.027","journal-title":"Neurocomputing"},{"key":"4284_CR21","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neucom.2021.03.110","volume":"448","author":"T Ouyang","year":"2021","unstructured":"Ouyang T (2021) Feature learning for stacked ELM via low-rank matrix factorization. Neurocomputing 448:82\u201393. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.110","journal-title":"Neurocomputing"},{"key":"4284_CR22","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.patcog.2018.07.011","volume":"84","author":"L Chen","year":"2018","unstructured":"Chen L, Honeine P, Qu H, Zhao J, Sun X (2018) Correntropy-based robust multilayer extreme learning machines. Pattern Recogn 84:357\u2013370. > https:\/\/doi.org\/10.1016\/j.patcog.2018.07.011","journal-title":"Pattern Recogn"},{"key":"4284_CR23","doi-asserted-by":"publisher","first-page":"107182","DOI":"10.1016\/j.knosys.2021.107182","volume":"227","author":"X Chen","year":"2021","unstructured":"Chen X, Wang Q, Zhuang S (2021) Ensemble dimension reduction based on spectral disturbance for subspace clustering. Knowl Based Syst 227:107182.  https:\/\/doi.org\/10.1016\/j.knosys.2021.107182","journal-title":"Knowl Based Syst"},{"issue":"3","key":"4284_CR24","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1177\/1748301817707321","volume":"11","author":"X Chen","year":"2017","unstructured":"Chen X, Liao M, Ye X (2017) Projection subspace clustering. J Algorithms Comput Technol 11(3):224\u2013233. https:\/\/doi.org\/10.1177\/1748301817707321","journal-title":"J Algorithms Comput Technol"},{"key":"4284_CR25","doi-asserted-by":"publisher","first-page":"22941","DOI":"10.1109\/ACCESS.2019.2893915","volume":"7","author":"Z Liu","year":"2019","unstructured":"Liu Z, Wang J, Liu G, Pu J (2019) Sparse low-rank preserving projection for dimensionality reduction. IEEE Access 7:22941\u201322951. https:\/\/doi.org\/10.1109\/ACCESS.2019.2893915","journal-title":"IEEE Access"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04284-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04284-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04284-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T03:57:55Z","timestamp":1685591875000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04284-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,19]]},"references-count":25,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4284"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04284-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,11,19]]},"assertion":[{"value":"19 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}