{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T17:21:06Z","timestamp":1764350466228,"version":"3.37.3"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"European Union s Horizon 2020 Research and Innovation Program","award":["646804-ERCCOG-BNYQ"],"award-info":[{"award-number":["646804-ERCCOG-BNYQ"]}]},{"DOI":"10.13039\/100000183","name":"US Army Research Office","doi-asserted-by":"crossref","award":["911NF-16-1-0265"],"award-info":[{"award-number":["911NF-16-1-0265"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["CCF-1319140","CCF-1718698"],"award-info":[{"award-number":["CCF-1319140","CCF-1718698"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Signal Process."],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1109\/jstsp.2018.2877405","type":"journal-article","created":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T19:49:57Z","timestamp":1540237797000},"page":"1240-1252","source":"Crossref","is-referenced-by-count":12,"title":["Subspace Estimation From Incomplete Observations: A High-Dimensional Analysis"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8327-9363","authenticated-orcid":false,"given":"Chuang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4358-5304","authenticated-orcid":false,"given":"Yonina C.","family":"Eldar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5174-2595","authenticated-orcid":false,"given":"Yue M.","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.2307\/3212147"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2015.7282771"},{"key":"ref31","first-page":"4520","article-title":"Provable efficient online matrix completion via non-convex stochastic gradient descent","author":"jin","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","first-page":"836","article-title":"Low-rank matrix and tensor completion via adaptive sampling","author":"krishnamurthy","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-009-9045-5"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/0165-1684(96)00008-4","article-title":"Asymptotic convergence analysis of the projection approximation subspace tracking algorithms","volume":"50","author":"yang","year":"1996","journal-title":"Signal Process"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1977.1101561"},{"journal-title":"Markov Processes Characterization and Convergence","year":"1985","author":"ethier","key":"ref10"},{"journal-title":"Convergence of Probability Measures","year":"2013","author":"billingsley","key":"ref11"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-05265-5","volume":"288","author":"jacod","year":"2003","journal-title":"Limit Theorems for Stochastic Processes"},{"key":"ref13","first-page":"186","article-title":"Online learning for sparse PCA in high dimensions: Exact dynamics and phase transitions","author":"wang","year":"0","journal-title":"Proc IEEE Inf Theory Workshop"},{"key":"ref14","first-page":"6641","article-title":"The scaling limit of high-dimensional online independent component analysis","author":"wang","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","article-title":"Scaling limit: Exact and tractable analysis of online learning algorithms with applications to regularized regression and PCA","author":"wang","year":"2017","journal-title":"arXiv 1712 04332"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2847041"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/78.365290"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2307\/2975268"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-8577-4_7"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2017.51"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-014-9227-7"},{"key":"ref27","first-page":"257","article-title":"Convergence of stochastic gradient descent for PCA","author":"shamir","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/BF00275687"},{"key":"ref6","first-page":"1460","article-title":"Global convergence of a Grassmannian gradient descent algorithm for subspace estimation","volume":"51","author":"zhang","year":"0","journal-title":"Proc 19th Int Conf Artif Intell"},{"key":"ref29","article-title":"Streaming PCA with many missing entries","author":"mitliagkas","year":"2014","journal-title":"Preprint"},{"key":"ref5","article-title":"Convergence of a Grassmannian gradient descent algorithm for subspace estimation from undersampled data","author":"zhang","year":"2016","journal-title":"arXiv 1610 00199"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(87)90184-0"},{"key":"ref7","first-page":"1","article-title":"Subspace learning with partial information","volume":"17","author":"gonen","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2282910"},{"key":"ref9","first-page":"165","article-title":"Topics in propagation of chaos","author":"sznitman","year":"0","journal-title":"Ecole d’Eté de Probabilités de Saint-Flour"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2010.5706976"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316962","author":"billingsley","year":"1999","journal-title":"Convergence of Probability Measures"},{"key":"ref22","first-page":"2861","article-title":"The noisy power method: A meta algorithm with applications","author":"hardt","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","first-page":"2886","article-title":"Memory limited, streaming PCA","author":"mitliagkas","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","article-title":"Near-optimal stochastic approximation for online principal component estimation","author":"li","year":"2016","journal-title":"arXiv 1603 05305"},{"key":"ref23","first-page":"284","article-title":"An improved gap-dependency analysis of the noisy power method","author":"balcan","year":"0","journal-title":"Proc Conf Learn Theory"},{"key":"ref26","first-page":"3174","article-title":"The fast convergence of incremental PCA","author":"balsubramani","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","first-page":"1147","article-title":"Streaming PCA: Matching matrix Bernstein and near-optimal finite sample guarantees for Oja's algorithm","author":"jain","year":"0","journal-title":"Proc Conf Learn Theory"}],"container-title":["IEEE Journal of Selected Topics in Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielaam\/4200690\/8566078\/8502097-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4200690\/8566078\/08502097.pdf?arnumber=8502097","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:52:08Z","timestamp":1649443928000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8502097\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12]]},"references-count":36,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/jstsp.2018.2877405","relation":{},"ISSN":["1932-4553","1941-0484"],"issn-type":[{"type":"print","value":"1932-4553"},{"type":"electronic","value":"1941-0484"}],"subject":[],"published":{"date-parts":[[2018,12]]}}}