{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T12:49:24Z","timestamp":1777466964618,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2019,2]]},"DOI":"10.1109\/tnnls.2018.2838140","type":"journal-article","created":{"date-parts":[[2018,6,21]],"date-time":"2018-06-21T18:53:49Z","timestamp":1529607229000},"page":"369-378","source":"Crossref","is-referenced-by-count":38,"title":["Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0427-977X","authenticated-orcid":false,"given":"Liang","family":"Lan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shandian","family":"Zhe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5456-626X","authenticated-orcid":false,"given":"Wei","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9692-4333","authenticated-orcid":false,"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"27","article-title":"Learning the kernel matrix with semidefinite programming","volume":"5","author":"lanckriet","year":"2004","journal-title":"J Mach Learn Res"},{"key":"ref38","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"},{"key":"ref33","first-page":"521","article-title":"Distance metric learning, with application to clustering with side-information","author":"xing","year":"2003","journal-title":"Proc Adv NIPS"},{"key":"ref32","first-page":"185","article-title":"Sharp analysis of low-rank kernel matrix approximations","author":"bach","year":"2013","journal-title":"Proc Conf Learn Theory"},{"key":"ref31","first-page":"304","article-title":"Sampling techniques for the Nystr&#x00F6;m method","author":"kumar","year":"2009","journal-title":"Proc 12th Int Conf Artif Intell Statist"},{"key":"ref30","first-page":"1425","article-title":"Scaling up kernel SVM on limited resources: A low-rank linearization approach","author":"zhang","year":"2012","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102356"},{"key":"ref36","first-page":"603","article-title":"A direct method for building sparse kernel learning algorithms","volume":"7","author":"wu","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972719.13"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1262185"},{"key":"ref10","first-page":"566","article-title":"A divide-and-conquer solver for kernel support vector machines","author":"hsieh","year":"2014","journal-title":"Proc 31st Int Conf Mach Learn"},{"key":"ref40","first-page":"341","article-title":"Low-rank kernel learning with Bregman matrix divergences","volume":"10","author":"kulis","year":"2009","journal-title":"J Mach Learn Res"},{"key":"ref11","first-page":"3103","article-title":"Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale SVM training","volume":"13","author":"wang","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015332"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273598"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150429"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390208"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2086737.2086743"},{"key":"ref17","first-page":"1471","article-title":"Training and testing low-degree polynomial data mappings via linear SVM","volume":"11","author":"chang","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref18","first-page":"999","article-title":"COFFIN: A computational framework for linear SVMs","author":"sonnenburg","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref19","first-page":"1177","article-title":"Random features for large-scale kernel machines","author":"rahimi","year":"2007","journal-title":"Proc Adv NIPS"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.820828"},{"key":"ref4","first-page":"185","article-title":"Fast training of support vector machines using sequential minimal optimization","author":"platt","year":"1999","journal-title":"Advances in Kernel Methods"},{"key":"ref27","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":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2342533"},{"key":"ref6","first-page":"363","article-title":"Core vector machines: Fast SVM training on very large data sets","volume":"6","author":"tsang","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020517"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2009.29"},{"key":"ref7","first-page":"1579","article-title":"Fast kernel classifiers with Online and active learning","volume":"6","author":"bordes","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2333879"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5126-6"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2472284"},{"key":"ref45","first-page":"459","article-title":"Fourier kernel learning","author":"b?z?van","year":"2012","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref22","first-page":"682","article-title":"Using the Nystr&#x00F6;m method to speed up kernel machines","author":"williams","year":"2001","journal-title":"Proc Adv NIPS"},{"key":"ref21","first-page":"476","article-title":"Nystr&#x00F6;m method vs random Fourier features: A theoretical and empirical comparison","author":"yang","year":"2012","journal-title":"Proc Adv NIPS"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020420"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2064786"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2334137"},{"key":"ref23","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":"ref44","first-page":"244","article-title":"Fastfood&#x2014;Computing Hilbert space expansions in loglinear time","author":"le","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098050"},{"key":"ref43","first-page":"3813","article-title":"BudgetedSVM: A toolbox for scalable SVM approximations","volume":"14","author":"djuric","year":"2013","journal-title":"J Mach Learn Res"},{"key":"ref25","first-page":"113","article-title":"On the impact of kernel approximation on learning accuracy","author":"cortes","year":"2010","journal-title":"Proc Int Conf Artif Intell Statist"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/8621620\/08392379.pdf?arnumber=8392379","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T21:14:39Z","timestamp":1657746879000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8392379\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2]]},"references-count":45,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2018.2838140","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2]]}}}