{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T01:08:16Z","timestamp":1783645696463,"version":"3.55.0"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2016,12,1]],"date-time":"2016-12-01T00:00:00Z","timestamp":1480550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/100013735","name":"Kentucky Science and Engineering Foundation","doi-asserted-by":"crossref","award":["KSEF-3113-RDE-017"],"award-info":[{"award-number":["KSEF-3113-RDE-017"]}],"id":[{"id":"10.13039\/100013735","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1109\/tnnls.2015.2479223","type":"journal-article","created":{"date-parts":[[2015,10,28]],"date-time":"2015-10-28T20:40:54Z","timestamp":1446064854000},"page":"2486-2498","source":"Crossref","is-referenced-by-count":186,"title":["Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints"],"prefix":"10.1109","volume":"27","author":[{"given":"Ehsan","family":"Hosseini-Asl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6622-534X","authenticated-orcid":false,"given":"Jacek M.","family":"Zurada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olfa","family":"Nasraoui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.1315150"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ACV.1994.341300"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/b98874"},{"key":"ref32","first-page":"950","article-title":"A simple weight decay can improve generalization","volume":"4","author":"krogh","year":"1995","journal-title":"Advances in neural information processing systems"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"ref30","first-page":"1339","article-title":"3D object recognition with deep belief nets","author":"nair","year":"2009","journal-title":"Advances in neural information processing systems"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref36","author":"schmidt","year":"2008","journal-title":"MATLAB software"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1137\/0916069"},{"key":"ref34","author":"zurada","year":"1992","journal-title":"Introduction to Artificial Neural Systems"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273592"},{"key":"ref40","first-page":"1457","article-title":"Non-negative matrix factorization with sparseness constraints","volume":"5","author":"hoyer","year":"2004","journal-title":"J Mach Learn Res"},{"key":"ref11","first-page":"625","article-title":"Why does unsupervised pre-training help deep learning?","volume":"11","author":"erhan","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"key":"ref14","first-page":"873","article-title":"Sparse deep belief net model for visual area V2","volume":"7","author":"lee","year":"2007","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/0896-6273(94)90455-3"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/4.5.509"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/S0042-6989(97)00169-7"},{"key":"ref19","first-page":"1137","article-title":"Efficient learning of sparse representations with an energy-based model","author":"ranzato","year":"2006","journal-title":"Advances in neural information processing systems"},{"key":"ref28","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"hinton","year":"2012"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1017\/atsip.2013.9"},{"key":"ref27","first-page":"556","article-title":"Algorithms for non-negative matrix factorization","author":"lee","year":"2000","journal-title":"Advances in neural information processing systems"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"hinton","year":"2006","journal-title":"Science"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.268"},{"key":"ref29","first-page":"3","article-title":"Autoencoders, minimum description length, and Helmholtz free energy","author":"hinton","year":"1994","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.118"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref7","first-page":"153","article-title":"Greedy layer-wise training of deep networks","volume":"19","author":"bengio","year":"2007","journal-title":"Advances in neural information processing systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref9","first-page":"1185","article-title":"Sparse feature learning for deep belief networks","volume":"20","author":"ranzato","year":"2007","journal-title":"Advances in neural information processing systems"},{"key":"ref1","first-page":"1","article-title":"Scaling learning algorithms towards AI","volume":"34","author":"bengio","year":"2007","journal-title":"Large Scale Kernel Machines"},{"key":"ref20","article-title":"k-sparse autoencoders","author":"makhzani","year":"2013"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"lee","year":"1999","journal-title":"Nature"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/381607a0"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1108\/eb026526"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2310059"},{"key":"ref41","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.05.003"},{"key":"ref26","first-page":"72","article-title":"Sparse autoencoder","author":"ng","year":"2011"},{"key":"ref43","author":"tan","year":"2005","journal-title":"Introduction to Data Mining"},{"key":"ref25","first-page":"133","article-title":"Learning parts-based representations with nonnegative restricted Boltzmann machine","author":"nguyen","year":"2013","journal-title":"Proc Asian Conf Mach Learn"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/7745987\/07310882.pdf?arnumber=7310882","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T02:34:32Z","timestamp":1633919672000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7310882\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12]]},"references-count":43,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2015.2479223","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12]]}}}