{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T05:19:27Z","timestamp":1775625567407,"version":"3.50.1"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2018YFB1107403"],"award-info":[{"award-number":["2018YFB1107403"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1864204"],"award-info":[{"award-number":["U1864204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773316"],"award-info":[{"award-number":["61773316"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1801262"],"award-info":[{"award-number":["U1801262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871470"],"award-info":[{"award-number":["61871470"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61761130079"],"award-info":[{"award-number":["61761130079"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of Special Zone for National Defense Science and Technology Innovation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Electron."],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1109\/tie.2020.2969072","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T21:42:02Z","timestamp":1580334122000},"page":"1684-1694","source":"Crossref","is-referenced-by-count":12,"title":["C2DNDA: A Deep Framework for Nonlinear Dimensionality Reduction"],"prefix":"10.1109","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7028-4956","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zequn","family":"Qin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0871-6519","authenticated-orcid":false,"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2924-946X","authenticated-orcid":false,"given":"Xuelong","family":"Li","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"1319","article-title":"Maxout networks","author":"goodfellow","year":"0","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref38","article-title":"Network in network","author":"lin","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2006.860"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989027"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2441735"},{"key":"ref36","first-page":"1482","article-title":"Bilinear classifiers for visual recognition","author":"pirsiavash","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.881945"},{"key":"ref34","first-page":"499","article-title":"Tensor subspace analysis","author":"he","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2004.09.007"},{"key":"ref40","article-title":"Spatially-sparse convolutional neural networks","author":"graham","year":"2014"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1261097"},{"key":"ref12","first-page":"1569","article-title":"Two-dimensional linear discriminant analysis","author":"ye","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2448547"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2007.11.025"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2475625"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/AICI.2009.491"},{"key":"ref17","article-title":"Deep linear discriminant analysis","author":"dorfer","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref18","first-page":"2929","article-title":"Convolutional 2DLDA for nonlinear dimensionality reduction","author":"wang","year":"0","journal-title":"Proc 26th Int Joint Conf Artif Intell"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.160"},{"key":"ref28","first-page":"212","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","volume":"3","author":"hinton","year":"2012","journal-title":"Comput Sci"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2009.2028255"},{"key":"ref27","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref3","first-page":"564","article-title":"Dual-space linear discriminant analysis for face recognition","author":"wang","year":"0","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2192285"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2437384"},{"key":"ref5","first-page":"1530","article-title":"Thinking of images as what they are: Compound matrix regression for image classification","author":"ma","year":"0","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref8","article-title":"Deep learning applied to NLP","author":"lopez","year":"2017"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TNNLS.2012.2230406","article-title":"Generalization performance of fisher linear discriminant based on Markov sampling","volume":"24","author":"zou","year":"2013","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref2","article-title":"Subspace linear discriminant analysis for face recognition","author":"zhao","year":"1999"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/34.598228"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TNNLS.2012.2194793","article-title":"Complexity-reduced scheme for feature extraction with linear discriminant analysis","volume":"23","author":"hou","year":"2012","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2306844"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2004.09.014"},{"key":"ref42","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","author":"sutskever","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref24","first-page":"1247","article-title":"Deep canonical correlation analysis","author":"andrew","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2013.287"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6637848"},{"key":"ref44","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","author":"abadi","year":"0","journal-title":"Proc Symp Oper Syst Des Implementation"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref43","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"}],"container-title":["IEEE Transactions on Industrial Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/41\/9248066\/08974600.pdf?arnumber=8974600","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:48:58Z","timestamp":1652194138000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8974600\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":44,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tie.2020.2969072","relation":{},"ISSN":["0278-0046","1557-9948"],"issn-type":[{"value":"0278-0046","type":"print"},{"value":"1557-9948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2]]}}}