{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T04:05:58Z","timestamp":1751256358596,"version":"3.41.0"},"reference-count":31,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1109\/allerton.2017.8262791","type":"proceedings-article","created":{"date-parts":[[2018,1,18]],"date-time":"2018-01-18T23:03:47Z","timestamp":1516316627000},"page":"596-603","source":"Crossref","is-referenced-by-count":1,"title":["Scalable kernel-based learning via low-rank approximation of lifted data"],"prefix":"10.1109","author":[{"given":"Fatemeh","family":"Sheikholeslami","sequence":"first","affiliation":[]},{"given":"Georgios B.","family":"Giannakis","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"ref30","first-page":"1871","article-title":"LIBLINEAR: A library for large linear classification","volume":"9","author":"fan","year":"2008","journal-title":"J of Mach Learn Res"},{"key":"ref10","first-page":"701","article-title":"Memory efficient kernel approximation","author":"si","year":"2014","journal-title":"Intl conf on Machine Learning"},{"key":"ref11","first-page":"1177","article-title":"Random features for large-scale kernel machines","author":"rahimi","year":"2007","journal-title":"Proc Adv Neu Inf Proc Sys"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1137\/060666998"},{"key":"ref13","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 of Mach Learn Res"},{"key":"ref14","first-page":"1882","article-title":"Fixed-budget kernel recursive least-zquares","author":"vaerenbergh","year":"2010","journal-title":"Proc Int Conf On Acoust Speech and Signal Proc"},{"key":"ref15","first-page":"1","article-title":"Large scale online kemel learning","volume":"17","author":"lu","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"ref16","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 Neu Inf Proc Sys"},{"key":"ref17","first-page":"1060","article-title":"Ensemble Nystr&#x00F6;m method","author":"kumar","year":"2009","journal-title":"Proc Adv Neu Inf Proc Sys"},{"key":"ref18","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"},{"key":"ref19","first-page":"185","article-title":"Sharp analysis of low-rank kernel matrix approximations","author":"bach","year":"2013","journal-title":"Conference on Learning Theory"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/BF01584660"},{"key":"ref4","first-page":"3041","article-title":"Scalable kernel methods via doubly stochastic gradients","author":"dai","year":"2014","journal-title":"Advances in Neural Inf Proc Systems"},{"journal-title":"Nonlinear Programming","year":"1999","author":"bertsekas","key":"ref27"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.830991"},{"key":"ref6","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 of Mach Learn Res"},{"journal-title":"Numerical Optimization","year":"1991","author":"nocedal","key":"ref29"},{"key":"ref5","first-page":"682","article-title":"Using the Nystr&#x00F6;m method to speed up kernel machines","author":"williams","year":"2001","journal-title":"Proc Adv Neu Inf Proc Sys"},{"key":"ref8","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":"ref7","first-page":"1232","article-title":"Improved Nystr&#x00F6;m low-rank approximation and error analysis","author":"zhang","year":"2008","journal-title":"Proc IEEE Intern Conf on Machine Learning"},{"key":"ref2","first-page":"243","article-title":"Efficient SVM training using low-rank kernel representations","volume":"2","author":"fine","year":"2002","journal-title":"J of Mach Learn Res"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1145\/2623330.2623614","article-title":"Improving the modified nystr&#x00F6;m method using spectral shifting","author":"wang","year":"2014","journal-title":"ACM Intl Conf on Knowledge Discovery and Data Mining"},{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","author":"scholkopf","year":"2001","journal-title":"Learning With Kernels Support Vector Machines Regularization Optimization and Beyond"},{"key":"ref20","first-page":"1648","article-title":"Less is more: Nystr&#x00F6;m computational regularization","author":"rudi","year":"2015","journal-title":"Proc Adv Neu Inf Proc Sys"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1137\/050626090"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1023\/A:1017501703105"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1137\/120891009"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s12532-013-0051-x"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2015.2424238"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2010.2069250"}],"event":{"name":"2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","start":{"date-parts":[[2017,10,3]]},"location":"Monticello, IL, USA","end":{"date-parts":[[2017,10,6]]}},"container-title":["2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8253908\/8262705\/08262791.pdf?arnumber=8262791","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,29]],"date-time":"2025-06-29T22:42:03Z","timestamp":1751236923000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8262791\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/allerton.2017.8262791","relation":{},"subject":[],"published":{"date-parts":[[2017,10]]}}}