{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:10Z","timestamp":1740160270908,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T00:00:00Z","timestamp":1578009600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T00:00:00Z","timestamp":1578009600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s13042-019-01057-7","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T16:03:05Z","timestamp":1578067385000},"page":"1557-1569","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Unsupervised feature learning with sparse Bayesian auto-encoding based extreme learning machine"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7879-773X","authenticated-orcid":false,"given":"Guanghao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongshun","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangbo","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang-Bin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"issue":"3","key":"1057_CR1","first-page":"1","volume":"5","author":"DE Rumelhart","year":"1988","unstructured":"Rumelhart DE, Hinton GE, Williams RJ et al (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1","journal-title":"Cognit Model"},{"issue":"4","key":"1057_CR2","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Chen L, Siew CK et al (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879\u2013892","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"1057_CR3","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2011","unstructured":"Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513\u2013529","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybernetics)"},{"issue":"1\u20133","key":"1057_CR4","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"3","key":"1057_CR5","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1109\/TPWRS.2008.926431","volume":"23","author":"AH Nizar","year":"2008","unstructured":"Nizar AH, Dong ZY, Wang Y (2008) Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst 23(3):946\u2013955","journal-title":"IEEE Trans Power Syst"},{"issue":"S8","key":"1057_CR6","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-14-S8-S10","volume":"14","author":"ZH You","year":"2013","unstructured":"You ZH, Lei YK, Zhu L, Xia J, Wang Bing (2013) Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. Bmc Bioinform 14(S8):S10","journal-title":"Bmc Bioinform"},{"issue":"7","key":"1057_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/cgf.12740","volume":"34","author":"X Zhige Xie","year":"2015","unstructured":"Zhige Xie X, Kai SW, Liu L, Xiong Y, Hui Huang (2015) Projective feature learning for 3D shapes with multi-view depth images. Comput Graphics Forum 34(7):1\u201311","journal-title":"Comput Graphics Forum"},{"issue":"12","key":"1057_CR8","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/TCYB.2014.2307349","volume":"44","author":"G Huang","year":"2014","unstructured":"Huang G, Song S, Gupta JND, Cheng W (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405\u20132417","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"1057_CR9","first-page":"31","volume":"28","author":"LLC Kasun","year":"2013","unstructured":"Kasun LLC, Zhou H, Huang G-B, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intell Syst 28(6):31\u201334","journal-title":"IEEE Intell Syst"},{"issue":"8","key":"1057_CR10","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1109\/TIP.2016.2570569","volume":"25","author":"LLC Kasun","year":"2016","unstructured":"Kasun LLC, Yang Y, Huang G-B, Zhang Z (2016) Dimension reduction with extreme learning machine. IEEE Trans Image Process 25(8):3906\u20133918","journal-title":"IEEE Trans Image Process"},{"key":"1057_CR11","unstructured":"Liyanaarachchi LCC, Tianchi L, Yan Y, Zhiping L, Guang-Bin H (2015) Extreme learning machine for clustering. In: Proceedings of ELM-2014, volume 1. Springer, pp 435\u2013444"},{"issue":"4","key":"1057_CR12","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"J Tang","year":"2015","unstructured":"Tang J, Deng C, Huang G-B (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809\u2013821","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"1057_CR13","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1109\/TNNLS.2013.2281839","volume":"25","author":"J Luo","year":"2013","unstructured":"Luo J, Vong C-M, Wong P-K (2013) Sparse bayesian extreme learning machine for multi-classification. IEEE Trans Neural Netw Learn Syst 25(4):836\u2013843","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"1057_CR14","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","volume":"12","author":"AE Hoerl","year":"1970","unstructured":"Hoerl AE, Kennard W (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55\u201367","journal-title":"Technometrics"},{"issue":"3","key":"1057_CR15","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","volume":"6","author":"G-B Huang","year":"2014","unstructured":"Huang G-B (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cognit Comput 6(3):376\u2013390","journal-title":"Cognit Comput"},{"key":"1057_CR16","doi-asserted-by":"crossref","unstructured":"Ding C, He X (2004) K-means clustering via principal component analysis. In: International Conference on machine learning","DOI":"10.1145\/1015330.1015408"},{"issue":"11","key":"1057_CR17","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","volume":"2","author":"K Pearson","year":"1901","unstructured":"Pearson K (1901) Liii. on lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci 2(11):559\u2013572","journal-title":"Lond Edinb Dublin Philos Mag J Sci"},{"issue":"6","key":"1057_CR18","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","volume":"24","author":"H Hotelling","year":"1933","unstructured":"Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417","journal-title":"J Educ Psychol"},{"key":"1057_CR19","doi-asserted-by":"crossref","unstructured":"Yoo Y, Oh S-Y (2016) Fast training of convolutional neural network classifiers through extreme learning machines. In: 2016 International Joint Conference on neural networks (IJCNN). IEEE, pp 1702\u20131708","DOI":"10.1109\/IJCNN.2016.7727403"},{"key":"1057_CR20","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1016\/j.neucom.2015.10.035","volume":"174","author":"Y Wang","year":"2016","unstructured":"Wang Y, Xie Z, Kai X, Dou Y, Lei Yuanwu (2016) An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning. Neurocomputing 174:988\u2013998","journal-title":"Neurocomputing"},{"key":"1057_CR21","unstructured":"Low C-Y, Teoh AB-J (2017) Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE, pp 1592\u20131596"},{"key":"1057_CR22","volume-title":"Bayesian statistical modelling","author":"P Congdon","year":"2007","unstructured":"Congdon P (2007) Bayesian statistical modelling, vol 704. Wiley, Hoboken"},{"issue":"3","key":"1057_CR23","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/TNN.2010.2103956","volume":"22","author":"E Soria-Olivas","year":"2011","unstructured":"Soria-Olivas E, Gomez-Sanchis J, Martin JD, Vila-Frances J, Martinez M, Magdalena JR, Serrano AJ (2011) Belm: Bayesian extreme learning machine. IEEE Trans Neural Netw 22(3):505\u2013509","journal-title":"IEEE Trans Neural Netw"},{"key":"1057_CR24","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin"},{"issue":"5","key":"1057_CR25","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1162\/neco.1992.4.5.720","volume":"4","author":"DJC MacKay","year":"1992","unstructured":"MacKay DJC (1992) The evidence framework applied to classification networks. Neural Comput 4(5):720\u2013736","journal-title":"Neural Comput"},{"issue":"3","key":"1057_CR26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S0129065704001930","volume":"14","author":"IT Nabney","year":"2004","unstructured":"Nabney IT (2004) Efficient training of RBF networks for classification. Int J Neural Syst 14(3):1\u20138","journal-title":"Int J Neural Syst"},{"issue":"Jun","key":"1057_CR27","first-page":"211","volume":"1","author":"ME Tipping","year":"2001","unstructured":"Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1(Jun):211\u2013244","journal-title":"J Mach Learn Res"},{"issue":"3","key":"1057_CR28","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","volume":"4","author":"DJC MacKay","year":"1992","unstructured":"MacKay DJC (1992) Bayesian interpolation. Neural Comput 4(3):415\u2013447","journal-title":"Neural Comput"},{"key":"1057_CR29","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M et al (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467"},{"issue":"6755","key":"1057_CR30","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee DD, Sebastian Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788","journal-title":"Nature"},{"key":"1057_CR31","unstructured":"Michael L, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Proceedings Third IEEE international conference on automatic face and gesture recognition, pp 200\u2013205. IEEE"},{"key":"1057_CR32","unstructured":"Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of 1994 IEEE Workshop on Applications of Computer Vision. IEEE, pp 138\u2013142"},{"key":"1057_CR33","doi-asserted-by":"crossref","unstructured":"Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. In: IEEE Conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2014.241"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-01057-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-019-01057-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-01057-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:17:34Z","timestamp":1609546654000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-019-01057-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,3]]},"references-count":33,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["1057"],"URL":"https:\/\/doi.org\/10.1007\/s13042-019-01057-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2020,1,3]]},"assertion":[{"value":"30 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}