{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:52:41Z","timestamp":1764053561102,"version":"3.37.3"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"21","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"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":["DMS-1317424","CCF-1350616"],"award-info":[{"award-number":["DMS-1317424","CCF-1350616"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-14-1-0342"],"award-info":[{"award-number":["FA9550-14-1-0342"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000879","name":"Alfred P. Sloan Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000879","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2018,11,1]]},"DOI":"10.1109\/tsp.2018.2869116","type":"journal-article","created":{"date-parts":[[2018,9,6]],"date-time":"2018-09-06T19:02:26Z","timestamp":1536260546000},"page":"5563-5576","source":"Crossref","is-referenced-by-count":2,"title":["A Unified Framework for Manifold Landmarking"],"prefix":"10.1109","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4192-5360","authenticated-orcid":false,"given":"Hongteng","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Licheng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6079-6328","authenticated-orcid":false,"given":"Mark A.","family":"Davenport","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyuan","family":"Zha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref71","first-page":"3019","article-title":"Manifold based dynamic\n texture synthesis from extremely few samples","author":"xu","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref70","first-page":"2208","article-title":"Manifold based face synthesis from sparse samples","author":"xu","year":"0","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref39","first-page":"1205","article-title":"Active transfer learning for cross-system recommendation","author":"zhao","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TSA.2005.848882"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2010.2042491"},{"key":"ref32","first-page":"839","article-title":"Less is more: Active learning with support vector machines","author":"schohn","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","first-page":"466","article-title":"Landmarking manifolds with Gaussian processes","author":"liang","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref30","first-page":"237","article-title":"Active learning literature survey","volume":"10","author":"settles","year":"1995","journal-title":"Science"},{"key":"ref37","first-page":"406","article-title":"Active\n learning for natural language parsing and information extraction","author":"thompson","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0721-9"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2411223"},{"key":"ref34","first-page":"56","article-title":"On the choice of kernel and labelled data in semi-supervised learning methods","author":"avrachenkov","year":"0","journal-title":"Proc 5th Int Workshop Algorithms Models Web-Graph"},{"key":"ref60","first-page":"1697","article-title":"Learning a\n discriminative dictionary for sparse coding via label consistent K-SVD","author":"jiang","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref62","first-page":"2288","article-title":"Hierarchical data-driven descent for efficient optimal deformation estimation","author":"tian","year":"0","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref61","first-page":"868","article-title":"Learning\n appearance manifolds from video","author":"rahimi","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref63","article-title":"The AR face database","author":"martinez","year":"1998","journal-title":"Computer Vision Center"},{"key":"ref28","first-page":"649","article-title":"Local and global regressive mapping for manifold learning with\n out-of-sample extrapolation","author":"yang","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2250353"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.11.1.15"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1613\/jair.295"},{"key":"ref67","first-page":"6351","article-title":"Learning combinatorial optimization algorithms over graphs","author":"khalil","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"article-title":"Attention solves your TSP","year":"2018","author":"kool","key":"ref68"},{"key":"ref69","first-page":"361","article-title":"Learning\n to traverse image manifolds","author":"doll\u00e1r","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref2","first-page":"1065","article-title":"Semi-supervised nonlinear dimensionality\n reduction","author":"yang","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref1","first-page":"2399","article-title":"Manifold\n regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"belkin","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.831130"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827502419154"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"roweis","year":"2000","journal-title":"Science"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780317"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global\n geometric framework for nonlinear dimensionality reduction","volume":"290","author":"tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2006.04.006"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2015.10.004"},{"key":"ref50","first-page":"1241","article-title":"Selecting\n landmark points for sparse manifold learning","author":"silva","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"article-title":"Diverse landmark sampling from determinantal point processes for scalable manifold learning","year":"2015","author":"wachinger","key":"ref51"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(75)90112-3"},{"key":"ref58","first-page":"95","article-title":"Convergence properties on the spline fit","volume":"11","author":"ahlberg","year":"1963","journal-title":"J SIAM"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.2307\/2153029"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1137\/100786022"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1137\/100795097"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1137\/050647554"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1137\/S0363012992238680"},{"journal-title":"Matrix Computations","year":"1996","author":"golub","key":"ref52"},{"key":"ref10","first-page":"749","article-title":"Über die abgrenzung der eigenwerte einer matrix","author":"ger\u0161gorin","year":"1931","journal-title":"Izv Akad Nauk USSR Otd Fiz -Mat Nauk"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-005-4965-4"},{"key":"ref40","first-page":"545","article-title":"Improved\n nonlinear manifold learning for land cover classification via intelligent landmark selection","author":"chen","year":"0","journal-title":"Proc IEEE Int Geosci Remote Sens Symp"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-005-4939-z"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/72.554192"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.842292"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/78.127958"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2007.896115"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2010.2047393"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2238935"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.2200\/S00196ED1V01Y200906AIM006"},{"key":"ref3","first-page":"1","article-title":"Spectral\n methods for semi-supervised manifold learning","author":"zhang","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000033120.25363.1e"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2090535"},{"key":"ref8","first-page":"58","article-title":"Combining active\n learning and semi-supervised learning using Gaussian fields and harmonic functions","author":"zhu","year":"0","journal-title":"Proc ICML Workshop Continuum Labeled to Unlabeled Data Machine Learning Data Mining"},{"key":"ref7","first-page":"3101","article-title":"Dictionary learning with mutually reinforcing group-graph structures","author":"xu","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref49","article-title":"Sparse multidimensional scaling using landmark points","volume":"120","author":"de silva","year":"2004"},{"key":"ref9","first-page":"3108","article-title":"Active manifold learning via Gershgorin circle guided\n sample selection","author":"xu","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref46","first-page":"981","article-title":"Sampling\n methods for the Nyström method","volume":"13","author":"kumar","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref45","first-page":"1232","article-title":"Improved\n Nyström low-rank approximation and error analysis","author":"zhang","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref48","first-page":"3087","article-title":"Unbiased estimates for linear regression via volume sampling","author":"derezinski","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref47","volume":"50","author":"pukelsheim","year":"1993","journal-title":"Optimal Design of Experiments"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1109\/LGRS.2014.2312619","article-title":"Active landmark sampling for manifold learning based spectral unmixing","volume":"11","author":"chi","year":"2014","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/LGRS.2012.2219613","article-title":"Selection of landmark points on nonlinear manifolds for spectral unmixing using local homogeneity","volume":"10","author":"chi","year":"2013","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539704442684"},{"key":"ref43","first-page":"2153","article-title":"On the Nyström method for approximating a Gram matrix for improved kernel-based learning","volume":"6","author":"drineas","year":"2005","journal-title":"J Mach Learn Res"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielaam\/78\/8470226\/8456613-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/8470226\/08456613.pdf?arnumber=8456613","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:48:53Z","timestamp":1649443733000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8456613\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,1]]},"references-count":71,"journal-issue":{"issue":"21"},"URL":"https:\/\/doi.org\/10.1109\/tsp.2018.2869116","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"type":"print","value":"1053-587X"},{"type":"electronic","value":"1941-0476"}],"subject":[],"published":{"date-parts":[[2018,11,1]]}}}