{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T21:58:58Z","timestamp":1780783138348,"version":"3.54.1"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2006844"],"award-info":[{"award-number":["IIS-2006844"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2144209"],"award-info":[{"award-number":["IIS-2144209"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2223769"],"award-info":[{"award-number":["IIS-2223769"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/tpami.2023.3311617","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T18:00:18Z","timestamp":1693850418000},"page":"15577-15587","source":"Crossref","is-referenced-by-count":11,"title":["Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4146-0436","authenticated-orcid":false,"given":"Han","family":"Yue","sequence":"first","affiliation":[{"name":"Michtom School of Computer Science, Brandeis University, Waltham, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-817X","authenticated-orcid":false,"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Department of Computer Science, and School of Data Science, University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0821-8640","authenticated-orcid":false,"given":"Hongfu","family":"Liu","sequence":"additional","affiliation":[{"name":"Michtom School of Computer Science, Brandeis University, Waltham, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783345"},{"key":"ref57","first-page":"1026","article-title":"Unsupervised feature selection using nonnegative spectral analysis","author":"li","year":"2012","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref12","first-page":"1589","article-title":"1-norm regularized discriminative feature selection for unsupervised learning","author":"yang","year":"2011","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835848"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.08.005"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.015"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.017"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.06.010"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1038\/73432"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2009.5178917"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.222"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220117"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273641"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.881945"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.1997.632300"},{"key":"ref16","article-title":"A second-order approach to learning with instance-dependent label noise","author":"zhu","year":"2020"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"ref18","first-page":"845","article-title":"Feature selection for unsupervised learning","volume":"5","author":"dy","year":"2004","journal-title":"J Mach Learn Res"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2007.02.014"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(91)90074-F"},{"key":"ref46","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00960"},{"key":"ref48","article-title":"Columbia object image library: Coil-20","author":"nene","year":"1996"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827595287997"},{"key":"ref42","author":"breiman","year":"1984","journal-title":"Classification and Regression Trees"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9277"},{"key":"ref44","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","author":"xie","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref43","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.96.12.6745"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/11564089_7"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and empirical analysis of ReliefF and RReliefF","volume":"53","author":"robnik-\u0161ikonja","year":"2003","journal-title":"Mach Learn"},{"key":"ref9","first-page":"507","article-title":"Laplacian score for feature selection","author":"he","year":"2005","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5991"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00306"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-66907-9"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-31573-5"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.03.004"},{"key":"ref35","article-title":"A note on the group Lasso and a sparse group Lasso","author":"friedman","year":"2010"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2477058"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.117"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462261"},{"key":"ref31","article-title":"Dropout feature ranking for deep learning models","author":"chang","year":"2017"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-022-3579-1"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2015.0189"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.040"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.4049\/jimmunol.1501557"},{"key":"ref1","first-page":"545","article-title":"Result analysis of the NIPS 2003 feature selection challenge","author":"guyon","year":"2004","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref39","first-page":"444","article-title":"Concrete autoencoders: Differentiable feature selection and reconstruction","author":"bal?n","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.10.130"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45571-X_13"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008992619036"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1080\/03610927408827101"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.07.026"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2002-6605"},{"key":"ref64","first-page":"8994","article-title":"SGCN: Sparse graph convolution network for pedestrian trajectory prediction","author":"shi","year":"2021","journal-title":"Proc IEEE Conf Comput Vis and Pattern Recog"},{"key":"ref63","article-title":"Part-based graph convolutional network for action recognition","author":"thakkar","year":"2018"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.71"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3002843"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/BIBE.2006.253339"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108299"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3136625"},{"key":"ref62","first-page":"6345","article-title":"On the stability of feature selection algorithms","volume":"18","author":"nogueira","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref61","first-page":"1027","article-title":"K-means: The advantages of careful seeding","author":"arthur","year":"0","journal-title":"Proc 18th Annu ACM-SIAM Symp Discrete Algorithms"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/10308548\/10238816-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10308548\/10238816.pdf?arnumber=10238816","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T19:33:04Z","timestamp":1743795184000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10238816\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":64,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3311617","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12]]}}}