{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:45:06Z","timestamp":1770741906801,"version":"3.49.0"},"reference-count":77,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62122013"],"award-info":[{"award-number":["62122013"]}],"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":["U2001211"],"award-info":[{"award-number":["U2001211"]}],"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":["61806044"],"award-info":[{"award-number":["61806044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tip.2021.3124317","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T19:20:58Z","timestamp":1636140058000},"page":"9280-9293","source":"Crossref","is-referenced-by-count":10,"title":["Deep Unsupervised Active Learning via Matrix Sketching"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5822-3408","authenticated-orcid":false,"given":"Changsheng","family":"Li","sequence":"first","affiliation":[]},{"given":"Rongqing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0181-8379","authenticated-orcid":false,"given":"Guoren","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2775-9730","authenticated-orcid":false,"given":"Dong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/1839490.1839495"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.12.019"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1152\/ajplung.00238.2017"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2785795"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2697767"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2011.10.002"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835930"},{"key":"ref38","first-page":"2016","article-title":"Sketching techniques for collaborative filtering","author":"bachrach","year":"2009","journal-title":"Proc Int Joint Conf Artif Intell (IJCAI)"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.39"},{"key":"ref33","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"2017","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref32","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc Annu Conf Neural Inf Process Syst (NIPS)"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2924023"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/566"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3183759"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2749443"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298865"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487623"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0119491"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(96)00034-3"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206744"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.04.022"},{"key":"ref64","author":"dua","year":"2017","journal-title":"UCI Machine Learning Repository"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2635440"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/1180639.1180727"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/364"},{"key":"ref67","first-page":"1107","article-title":"Matching words and pictures","volume":"3","author":"barnard","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref68","article-title":"Classifying yelp reviews into relevant categories","author":"sajnani","year":"2012"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623698"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2180916"},{"key":"ref1","first-page":"441","article-title":"Toward optimal active learning through Monte Carlo estimation of error reduction","author":"roy","year":"2001","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/TIP.2009.2032342","article-title":"Laplacian regularized D-optimal design for active learning and its application to image retrieval","volume":"19","author":"he","year":"2010","journal-title":"IEEE Trans Image Process"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.104"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.20"},{"key":"ref24","first-page":"1415","article-title":"Active learning via neighborhood reconstruction","author":"hu","year":"2013","journal-title":"Proc Int Joint Conf Artif Intell (IJCAI)"},{"key":"ref23","first-page":"1572","article-title":"Early active learning via robust representation and structured sparsity","author":"nie","year":"2013","journal-title":"Proc Int Joint Conf Artif Intell (IJCAI)"},{"key":"ref26","first-page":"1997","article-title":"Diversifying convex transductive experimental design for active learning","author":"shi","year":"2016","journal-title":"Proc Int Joint Conf Artif Intell (IJCAI)"},{"key":"ref25","first-page":"3217","article-title":"10,000+ times accelerated robust subset selection (ARSS)","author":"zhu","year":"2015","journal-title":"Proc 29th AAAI Conf Artif Intell (AAAI)"},{"key":"ref50","first-page":"406","article-title":"Column selection via adaptive sampling","author":"paul","year":"2015","journal-title":"Proc 28th Int Conf Neural Inf Process Syst"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1137\/15M1009718"},{"key":"ref59","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref58","first-page":"2153","article-title":"On the Nystr&#x00F6;m method for approximating a Gram matrix for improved kernel-based learning","volume":"6","author":"drineas","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref57","first-page":"223","article-title":"Pass efficient algorithms for approximating large matrices","volume":"3","author":"drineas","year":"2003","journal-title":"Proc SODA"},{"key":"ref56","first-page":"467","article-title":"Sketching transformed matrices with applications to natural language processing","author":"liang","year":"2020","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref55","first-page":"8080","article-title":"Efficient anomaly detection via matrix sketching","author":"sharan","year":"2018","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst"},{"key":"ref54","first-page":"1","article-title":"Column subset selection with missing data","volume":"1","author":"balzano","year":"2010","journal-title":"Proc NIPS Workshop Low-Rank Methods Large-Scale Mach Learn"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/0167-6423(82)90012-0"},{"key":"ref52","first-page":"567","article-title":"Co-occurring directions sketching for approximate matrix multiply","author":"mroueh","year":"2017","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/343"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1219092.1219097"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2867913"},{"key":"ref12","first-page":"5976","article-title":"Deep active learning with a neural architecture search","author":"geifman","year":"2019","journal-title":"Proc Annu Conf Neural Inf Process Syst (NIPS)"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015385"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/261"},{"key":"ref15","first-page":"1","article-title":"Active learning for convolutional neural networks: A core-set approach","author":"sener","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref16","first-page":"253","article-title":"Joint transfer and batch-mode active learning","author":"chattopadhyay","year":"2013","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref17","first-page":"1308","article-title":"Deep active learning: Unified and principled method for query and training","author":"shui","year":"2020","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143980"},{"key":"ref19","first-page":"635","article-title":"Non-greedy active learning for text categorization using convex ansductive experimental design","author":"yu","year":"2008","journal-title":"Proc Int ACM SIGIR Conf Res Develop Inf Retr"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2302675"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2327805"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2945679"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2700408"},{"key":"ref8","first-page":"593","article-title":"Discriminative batch mode active learning","author":"guo","year":"2007","journal-title":"Proc Annu Conf Neural Inf Process Syst (NIPS)"},{"key":"ref49","first-page":"1","article-title":"Towards a zero-one law for column subset selection","author":"song","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2840980"},{"key":"ref9","first-page":"3970","article-title":"Bayesian active learning by soft mean objective cost of uncertainty","author":"zhao","year":"2021","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/11830924_26"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipl.2011.01.010"},{"key":"ref48","first-page":"1774","article-title":"Subset selection by Pareto optimization","volume":"28","author":"qian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/1039488.1039494"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1090\/dimacs\/065"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973068.105"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0709640104"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2006.37"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9263394\/09605212.pdf?arnumber=9605212","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:08Z","timestamp":1652194208000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9605212\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":77,"URL":"https:\/\/doi.org\/10.1109\/tip.2021.3124317","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}