{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T02:31:20Z","timestamp":1768789880319,"version":"3.49.0"},"reference-count":101,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008628","name":"Ministry of Electronics and Information Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008628","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Mathematical Research Impact Centric Support"},{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001843","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":[[2022,12,1]]},"DOI":"10.1109\/tpami.2021.3122259","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T20:53:53Z","timestamp":1635281633000},"page":"8992-9010","source":"Crossref","is-referenced-by-count":31,"title":["Iteratively Reweighted Minimax-Concave Penalty Minimization for Accurate Low-rank Plus Sparse Matrix Decomposition"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5513-5891","authenticated-orcid":false,"given":"Praveen Kumar","family":"Pokala","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Science, Bengaluru, Karnataka, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0272-9714","authenticated-orcid":false,"given":"Raghu Vamshi","family":"Hemadri","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, Telangana, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9049-1912","authenticated-orcid":false,"given":"Chandra Sekhar","family":"Seelamantula","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Science, Bengaluru, Karnataka, India"}]}],"member":"263","reference":[{"key":"ref39","author":"boyd","year":"2011","journal-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers"},{"key":"ref38","first-page":"1764","article-title":"Nonconvex relaxation approaches to robust matrix recovery","author":"wang","year":"2013","journal-title":"Proc 23rd Intl Joint Conf Artif Intell"},{"key":"ref33","first-page":"655","article-title":"Low-rank matrix recovery via efficient Schatten $p$p-norm minimization","author":"nie","year":"2012","journal-title":"Proc 26th AAAI Conf Artif Intell"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v25i1.7921"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382273"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS729"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI45749.2020.9098517"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2511584"},{"key":"ref35","first-page":"3590","article-title":"Robust dictionary learning with capped $\\ell _1$?1-norm","author":"jiang","year":"2015","journal-title":"Proc 24th Int Joint Conf Artif Intell"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2298839"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.271"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9482"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2711501"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2853498"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1109\/JSTSP.2018.2879245","article-title":"Introduction to the issue on robust subspace learning and tracking: Theory, algorithms, and applications","volume":"12","author":"bouwmans","year":"2018","journal-title":"IEEE J Sel Top Signal Process"},{"key":"ref24","first-page":"2080","article-title":"Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization","author":"wright","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1177153"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/34.969114"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1137\/070697835"},{"key":"ref100","first-page":"1568","article-title":"Incremental gradient on the grassmannian for online foreground and background separation in subsampled video","author":"he","year":"2012","journal-title":"Proc Conf Comput Vis and Pattern Recog"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539792240406"},{"key":"ref50","first-page":"1350","article-title":"Efficient structured matrix rank minimization","author":"yu","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299078"},{"key":"ref59","article-title":"Analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation","author":"zha","year":"2017"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.366"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2937282"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0904-7"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2535227"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2020.2990948"},{"key":"ref53","first-page":"4002","article-title":"Accelerated inexact soft-impute for fast large-scale matrix completion","author":"yao","year":"2015","journal-title":"Proc 24th Intl Joint Conf Artif Intell"},{"key":"ref52","first-page":"2287","article-title":"Spectral regularization algorithms for learning large incomplete matrices","volume":"11","author":"mazumder","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.09.021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1137\/080738970"},{"key":"ref3","first-page":"5","article-title":"Improving regularized singular value decomposition for collaborative filtering","author":"paterek","year":"2007","journal-title":"Proc KDD Cup Workshop"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2748590"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2016.2598574"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2158250"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-010-0437-8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2212015"},{"key":"ref9","first-page":"1930","article-title":"Speeding up latent variable gaussian graphical model estimation via nonconvex optimization","author":"xu","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553434"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2001.945730"},{"key":"ref48","first-page":"615","article-title":"An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems","volume":"6","author":"toh","year":"2010","journal-title":"Pacific J Optim"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1561\/2400000003"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-012-0515-x"},{"key":"ref41","first-page":"703","article-title":"Robust photometric stereo via low-rank matrix completion and recovery","author":"wu","year":"2010","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.03.046"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2361338"},{"key":"ref73","first-page":"1107","article-title":"Non-convex robust PCA","author":"netrapalli","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref72","first-page":"1","article-title":"Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix","author":"lin","year":"2009","journal-title":"Proc IEEE Intl Workshop Comput Adv Multi-Sensor Adaptive Process"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2846606"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1201\/b20190-24"},{"key":"ref76","article-title":"A non-convex approach to low-rank and sparse matrix decomposition","author":"cui","year":"2018"},{"key":"ref77","article-title":"Nonconvex approach for sparse and low rank constrained models with dual momentum","author":"wu","year":"2019"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.2007606"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.073"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2208955"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/SSP.2018.8450718"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803145"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.526"},{"key":"ref61","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s00041-008-9045-x","article-title":"Enhancing sparsity by reweighted $\\ell _1$?1 minimization","volume":"14","author":"cand\u00e8s","year":"2008","journal-title":"J Fourier Anal Appl"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2957925"},{"key":"ref64","article-title":"The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices","author":"lin","year":"2010"},{"key":"ref65","first-page":"1089","article-title":"SpaRCS: Recovering low-rank and sparse matrices from compressive measurements","author":"waters","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1137\/090761793"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25240"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2923816"},{"key":"ref2","first-page":"82","article-title":"Local low-rank matrix approximation","author":"lee","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-018-1555-1"},{"key":"ref1","first-page":"10","article-title":"Rank selection in low-rank matrix approximations: A study of cross-validation for NMFs","author":"kanagal","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref95","year":"0"},{"key":"ref94","year":"0"},{"key":"ref93","year":"0"},{"key":"ref92","year":"0"},{"key":"ref91","first-page":"1081","article-title":"Analysis of multi-stage convex relaxation for sparse regularization.","volume":"11","author":"zhang","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.10.003"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2012.6238919"},{"key":"ref99","first-page":"291","article-title":"A benchmark dataset for outdoor foreground\/background extraction","author":"vacavant","year":"2012","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref96","first-page":"1410","article-title":"Practical low-rank matrix approximation under robust $\\ell _1$?1-norm","author":"zheng","year":"2012","journal-title":"Proc Conf Comput Vis and Pattern Recog"},{"key":"ref97","first-page":"55","article-title":"Robust principal component analysis with complex noise","author":"zhao","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2016.11.001"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-009-9045-5"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2013.08.006"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0930-5"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2599290"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2708981"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2018.2872023"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2015.7301753"},{"key":"ref17","first-page":"37","article-title":"Fast global convergence rates of gradient methods for high-dimensional statistical recovery","author":"agarwal","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_35"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2462360"},{"key":"ref84","first-page":"314","article-title":"Foreground segmentation via dynamic tree-structured sparse RPCA","author":"ebadi","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2015.123"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_50"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-015-0610-z"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7899619"},{"key":"ref86","first-page":"1170","article-title":"Foreground detection with weighted Schatten-$p$p norm and 3D total variation","volume":"39","author":"chen","year":"2019","journal-title":"J Comput Appl"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1145\/3407188"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461540"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9940447\/09585422.pdf?arnumber=9585422","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T23:12:14Z","timestamp":1670281934000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9585422\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":101,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2021.3122259","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":[[2022,12,1]]}}}