{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:16:58Z","timestamp":1783610218340,"version":"3.55.0"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"name":"Research Grants Council, Hong Kong","award":["614012"],"award-info":[{"award-number":["614012"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61272248"],"award-info":[{"award-number":["61272248"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"crossref","award":["2013CB329401"],"award-info":[{"award-number":["2013CB329401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["13511500200"],"award-info":[{"award-number":["13511500200"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011102","name":"European Union Seventh Framework Programme","doi-asserted-by":"crossref","award":["247619"],"award-info":[{"award-number":["247619"]}],"id":[{"id":"10.13039\/100011102","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2015,1]]},"DOI":"10.1109\/tnnls.2014.2359798","type":"journal-article","created":{"date-parts":[[2014,10,8]],"date-time":"2014-10-08T18:41:06Z","timestamp":1412793666000},"page":"152-164","source":"Crossref","is-referenced-by-count":65,"title":["Large-Scale Nystr\u00f6m Kernel Matrix Approximation Using Randomized SVD"],"prefix":"10.1109","volume":"26","author":[{"family":"Mu Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Wei Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James T.","family":"Kwok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Bao-Liang Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"301","article-title":"Loose synchronization for large-scale networked systems","author":"albrecht","year":"2006","journal-title":"Proc USENIX Annu Tech Conf"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2008.5214359"},{"key":"ref33","first-page":"567","article-title":"Revisiting the Nystr&#x00F6;m method for improved large-scale machine learning","author":"gittens","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref32","first-page":"3475","article-title":"Fast approximation of matrix coherence and statistical leverage","volume":"13","author":"drineas","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1137\/07070471X"},{"key":"ref30","first-page":"1232","article-title":"Improved Nystr&#x00F6;m low-rank approximation and error analysis","author":"zhang","year":"2008","journal-title":"Proc 25th Int Conf Mach Learn"},{"key":"ref37","first-page":"476","article-title":"Nystr&#x00F6;m method vs random Fourier features: A theoretical and empirical comparison","author":"yang","year":"2012","journal-title":"Advances in Neural Information Processing Systems 25"},{"key":"ref36","article-title":"Efficient algorithms and error analysis for the modified Nystr&#x00F6;m method","author":"wang","year":"2014"},{"key":"ref35","first-page":"572","article-title":"Matrix coherence and the Nystr&#x00F6;m method","author":"talwalkar","year":"2010","journal-title":"Proc 26th Int Conf Uncertainty Artif Intell"},{"key":"ref34","first-page":"534","article-title":"Can matrix coherence be efficiently and accurately estimated?","author":"mohri","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref10","author":"baker","year":"1977","journal-title":"The Numerical Treatment of Integral Equations"},{"key":"ref40","author":"kontoghiorghes","year":"2010","journal-title":"Handbook of Parallel Computing and Statistics"},{"key":"ref11","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":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1262185"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587670"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/223784.223812"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312264"},{"key":"ref16","first-page":"721","article-title":"Global versus local methods in nonlinear dimensionality reduction","author":"de silva","year":"2003","journal-title":"Advances in Neural Information Processing Systems 15"},{"key":"ref17","first-page":"1","article-title":"FastMap, MetricMap, and landmark MDS are all Nystr&#x00F6;m algorithms","author":"platt","year":"2005","journal-title":"Proc 10th Int Workshop Artif Intell Statist"},{"key":"ref18","article-title":"Semi-supervised learning using sparse eigenfunction bases","author":"sinha","year":"2010","journal-title":"Advances in Neural Information Processing Systems 22"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.873276"},{"key":"ref28","first-page":"253","article-title":"Greedy spectral embedding","author":"ouimet","year":"2005","journal-title":"Proc 10th Int Workshop Artif Intell Statist"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"ref27","first-page":"981","article-title":"Sampling methods for the Nystr&#x00F6;m method","volume":"13","author":"kumar","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/NNSP.1999.788121"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref29","first-page":"1","article-title":"A novel greedy algorithm for Nystr&#x00F6;m approximation","author":"farahat","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref5","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","author":"belkin","year":"2002","journal-title":"Advances in Neural Information Processing Systems 14"},{"key":"ref8","article-title":"Using the Nystr&#x00F6;m method to speed up kernel machines","author":"williams","year":"2001","journal-title":"Advances in Neural Information Processing Systems 13"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719628"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1137\/090771806"},{"key":"ref1","author":"sch\u00f6lkopf","year":"2002","journal-title":"Learning with kernels"},{"key":"ref20","first-page":"223","article-title":"ASSET: Approximate stochastic subgradient estimation training for support vector machines","author":"lee","year":"2012","journal-title":"Proc Int Conf Pattern Recognit Appl Methods"},{"key":"ref22","article-title":"Ensemble Nystr&#x00F6;m method","author":"kumar","year":"2010","journal-title":"Advances in Neural Information Processing Systems 22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2064786"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2640087.2644155"},{"key":"ref24","first-page":"631","article-title":"Making large-scale Nystr&#x00F6;m approximation possible","author":"li","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref41","article-title":"Parameter server for distributed machine learning","author":"li","year":"2013","journal-title":"NIPS Big Learning Workshop"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539704442696"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539704442684"},{"key":"ref43","author":"stewart","year":"1990","journal-title":"Matrix Perturbation Theory"},{"key":"ref25","first-page":"305","article-title":"Inductive kernel low-rank decomposition with priors: A generalized Nystr&#x00F6;m method","author":"zhang","year":"2012","journal-title":"Proc 29th Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/6990695\/06918503.pdf?arnumber=6918503","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:00:47Z","timestamp":1642003247000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/6918503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,1]]},"references-count":43,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2014.2359798","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,1]]}}}