{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T23:43:52Z","timestamp":1768520632146,"version":"3.49.0"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2015,3,1]],"date-time":"2015-03-01T00:00:00Z","timestamp":1425168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"name":"National Institute of Health","award":["R01-CA140663"],"award-info":[{"award-number":["R01-CA140663"]}]},{"DOI":"10.13039\/100006235","name":"Lawrence Berkeley National Laboratory","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006235","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hong Kong Competitive Earmarked Research","award":["614012"],"award-info":[{"award-number":["614012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2015,3]]},"DOI":"10.1109\/tnnls.2014.2315526","type":"journal-article","created":{"date-parts":[[2014,4,21]],"date-time":"2014-04-21T18:01:26Z","timestamp":1398103286000},"page":"444-457","source":"Crossref","is-referenced-by-count":40,"title":["Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines"],"prefix":"10.1109","volume":"26","author":[{"given":"Kai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Lan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James T.","family":"Kwok","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Slobodan","family":"Vucetic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bahram","family":"Parvin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3115\/981658.981684"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1109\/TNN.2010.2047114","article-title":"Discriminative semi-supervised feature selection via manifold regularization","volume":"21","author":"xu","year":"2010","journal-title":"IEEE Trans Neural Netw"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2214488"},{"key":"ref32","first-page":"1513","article-title":"Unlabeled data: Now it helps, now it doesn't","author":"singh","year":"2008","journal-title":"Proc Adv NIPS"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"ref30","author":"shewchuk","year":"1994","journal-title":"An Introduction to the Conjugate Gradient Method Without the Agonizing Pain"},{"key":"ref37","first-page":"682","article-title":"Using the Nystr&#x00F6;m method to speed up kernel machines","volume":"13","author":"williams","year":"2001","journal-title":"Proc Adv NIPS"},{"key":"ref36","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":"ref35","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"tibshirani","year":"1996","journal-title":"J Roy Statist Soc B"},{"key":"ref34","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":"ref28","first-page":"1369","article-title":"Generalization error bounds in semi-supervised classification under the cluster assumption","volume":"8","author":"rigollet","year":"2007","journal-title":"J Mach Learn Res"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007692713085"},{"key":"ref29","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":"ref2","first-page":"854","article-title":"Probabilistic modeling for face orientation discrimination: Learning from labeled and unlabeled data","volume":"11","author":"baluja","year":"1999","journal-title":"Proc Adv NIPS"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102356"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015337"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2190420"},{"key":"ref21","article-title":"Learning to model spatial dependency: Semi-supervised discriminative random fields","volume":"19","author":"lee","year":"2007","journal-title":"Proc Adv NIPS"},{"key":"ref24","first-page":"1149","article-title":"Laplacian support vector machines trained in the primal","volume":"12","author":"melacci","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref23","first-page":"679","article-title":"Large graph construction for scalable semi-supervised learning","author":"liu","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2011.2178037"},{"key":"ref25","first-page":"849","article-title":"On spectral clustering: Analysis and an algorithm","volume":"14","author":"ng","year":"2001","journal-title":"Proc Adv NIPS"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"ref11","article-title":"Semi-supervised classification by low density separation","author":"chapelle","year":"2005","journal-title":"Proc 10th Int Workshop Artif Intell Statist"},{"key":"ref40","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":"ref12","first-page":"921","article-title":"The importance of encoding versus training with sparse coding and vector quantization","author":"coates","year":"2011","journal-title":"Proc 28th Int Conf Mach Learn"},{"key":"ref13","first-page":"96","article-title":"Efficient non-parametric function induction in semi-supervised learning","author":"delalleau","year":"2005","journal-title":"Proc 10th Int Workshop Artif Intell Statist"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539704442696"},{"key":"ref15","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":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1262185"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1998.743487"},{"key":"ref18","first-page":"505","article-title":"Hierarchical clustering of a mixture model","volume":"17","author":"goldberger","year":"2005","journal-title":"Proc Adv NIPS"},{"key":"ref19","first-page":"200","article-title":"Transductive inference for text classification using support vector machines","author":"joachims","year":"1999","journal-title":"Proc 16th Int Conf Mach Learn"},{"key":"ref4","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"belkin","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref3","first-page":"585","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","author":"belkin","year":"2002","journal-title":"Proc Adv NIPS"},{"key":"ref6","first-page":"19","article-title":"Learning from labeled and unlabeled data using graph mincuts","author":"blum","year":"2001","journal-title":"Proc 18th Int Conf Mach Learn"},{"key":"ref5","first-page":"33","article-title":"Does unlabeled data provably help? Worst-case analysis of the sample complexity of semi-supervised learning","author":"ben-david","year":"2008","journal-title":"Proc of the 21st Annual Conference on Learning Theory"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.895416"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102484"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(94)00074-D"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2186825"},{"key":"ref45","first-page":"321","article-title":"Learning with local and global consistency","volume":"16","author":"zhou","year":"2003","journal-title":"Proc Adv NIPS"},{"key":"ref48","first-page":"912","article-title":"Semi-supervised learning using Gaussian fields and harmonic functions","author":"zhu","year":"2003","journal-title":"Proc 20th Int Conf Mach Learn"},{"key":"ref47","author":"zhu","year":"2008","journal-title":"Semi-supervised learning literature survey"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553531"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2064786"},{"key":"ref44","first-page":"388","article-title":"Covariate shift in Hilbert space: A solution via surrogate kernels","author":"zhang","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref43","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"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/ieeexplore.ieee.org\/iel7\/5962385\/7042870\/06803073.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/7042870\/06803073.pdf?arnumber=6803073","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T02:54:15Z","timestamp":1716692055000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/6803073\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,3]]},"references-count":49,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2014.2315526","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,3]]}}}