{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T23:24:21Z","timestamp":1720049061034},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2016,7,1]],"date-time":"2016-07-01T00:00:00Z","timestamp":1467331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition Letters"],"published-print":{"date-parts":[[2016,7]]},"DOI":"10.1016\/j.patrec.2016.03.031","type":"journal-article","created":{"date-parts":[[2016,4,10]],"date-time":"2016-04-10T10:03:14Z","timestamp":1460282594000},"page":"50-57","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Kernel matrix decomposition via empirical kernel map"],"prefix":"10.1016","volume":"77","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8508-8544","authenticated-orcid":false,"given":"Sanparith","family":"Marukatat","sequence":"first","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.patrec.2016.03.031_bib0001","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/S0022-0000(03)00025-4","article-title":"Database-friendly random projections: Johnson\u2013Lindenstrauss with binary coins","volume":"66","author":"Achlioptas","year":"2003","journal-title":"J. Comput. Syst. Sci."},{"key":"10.1016\/j.patrec.2016.03.031_bib0002","series-title":"Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, Vancouver, British Columbia, Canada, December 3\u20138, 2001]","first-page":"335","article-title":"Sampling techniques for kernel methods","author":"Achlioptas","year":"2001"},{"key":"10.1016\/j.patrec.2016.03.031_bib0003","series-title":"Proceedings of the 29th International Colloquium on Automata, Languages and Programming, ICALP\u201902","first-page":"693","article-title":"Finding frequent items in data streams","author":"Charikar","year":"2002"},{"issue":"6","key":"10.1016\/j.patrec.2016.03.031_bib0004","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/TIP.2007.896668","article-title":"Incremental kernel principal component analysis","volume":"16","author":"Chin","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patrec.2016.03.031_bib0005","first-page":"2153","article-title":"On the Nystr\u00f6m method for approximating a gram matrix for improved kernel-based learning","volume":"6","author":"Drineas","year":"2005","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"10.1016\/j.patrec.2016.03.031_bib0006","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1109\/TPAMI.2004.1262185","article-title":"Spectral grouping using the Nystr\u00f6m method","volume":"26","author":"Fowlkes","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patrec.2016.03.031_bib0007","series-title":"Proceedings of the 10th International Conference on Computer Analysis of Images and Patterns","first-page":"426","article-title":"Greedy algorithm for a training set reduction in the kernel methods","author":"Franc","year":"2003"},{"key":"10.1016\/j.patrec.2016.03.031_bib0008","series-title":"Matrix Computations","author":"Golub","year":"1996"},{"key":"10.1016\/j.patrec.2016.03.031_bib0009","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1090\/conm\/026\/737400","article-title":"Extensions of Lipschitz mapping into a Hilbert space","volume":"26","author":"Johnson","year":"1984","journal-title":"Contemp. Math."},{"issue":"9","key":"10.1016\/j.patrec.2016.03.031_bib0010","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TPAMI.2005.181","article-title":"Iterative kernel principal component analysis for image modeling","volume":"27","author":"Kim","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.patrec.2016.03.031_bib0011","first-page":"981","article-title":"Sampling methods for the Nystr\u00f6m method","volume":"13","author":"Kumar","year":"2012","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"10.1016\/j.patrec.2016.03.031_bib0012","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1109\/TNN.2004.837781","article-title":"The pre-image problem in kernel methods","volume":"15","author":"Kwok","year":"2004","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patrec.2016.03.031_bib0013","series-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel, June 21\u201324, 2010","first-page":"631","article-title":"Making large-scale Nystr\u00f6m approximation possible","author":"Li","year":"2010"},{"key":"10.1016\/j.patrec.2016.03.031_bib0014","series-title":"Proceedings of PRICAI 2006: Trends in Artificial Intelligence, 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, August 7\u201311, 2006","first-page":"454","article-title":"Sparse kernel PCA by kernel k-means and preimage reconstruction algorithms","author":"Marukatat","year":"2006"},{"key":"10.1016\/j.patrec.2016.03.031_bib0015","series-title":"Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II","first-page":"536","article-title":"Kernel PCA and de-noising in feature spaces","author":"Mika","year":"1999"},{"issue":"3","key":"10.1016\/j.patrec.2016.03.031_bib0016","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/BF00275687","article-title":"Simplified neuron model as a principal component analyzer","volume":"15","author":"Oja","year":"1982","journal-title":"J. Math. Biol."},{"key":"10.1016\/j.patrec.2016.03.031_bib0017","series-title":"Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD\u201913","first-page":"239","article-title":"Fast and scalable polynomial kernels via explicit feature maps","author":"Pham","year":"2013"},{"key":"10.1016\/j.patrec.2016.03.031_bib0018","series-title":"Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, Vancouver and Whistler, British Columbia, Canada, December 8\u201313, 2003]","first-page":"571","article-title":"Fast embedding of sparse similarity graphs","author":"Platt","year":"2003"},{"key":"10.1016\/j.patrec.2016.03.031_bib0019","series-title":"Advances in Neural Information Processing Systems 20: Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007","first-page":"1177","article-title":"Random features for large-scale kernel machines","author":"Rahimi","year":"2007"},{"key":"10.1016\/j.patrec.2016.03.031_bib0020","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0893-6080(89)90044-0","article-title":"Optimal unsupervised learning in a single-layer linear feedforward neural network.","volume":"2","author":"Sanger","year":"1989","journal-title":"Neural Netw."},{"issue":"5","key":"10.1016\/j.patrec.2016.03.031_bib0021","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/72.788641","article-title":"Input space versus feature space in kernel-based methods","volume":"10","author":"Sch\u00f6lkopf","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patrec.2016.03.031_sbref0022","series-title":"Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, AISTATS 2009, Clearwater Beach, Florida, USA, April 16\u201318, 2009","first-page":"496","article-title":"Hash kernels","author":"Shi","year":"2009"},{"key":"10.1016\/j.patrec.2016.03.031_bib0023","series-title":"Proceedings of the Seventeenth International Conference on Machine Learning, ICML\u201900","first-page":"911","article-title":"Sparse greedy matrix approximation for machine learning","author":"Smola","year":"2000"},{"key":"10.1016\/j.patrec.2016.03.031_bib0024","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1137\/1015095","article-title":"Error and perturbation bounds for subspaces associated with certain eigenvalue problems","volume":"15","author":"Stewart","year":"1973","journal-title":"SIAM Rev."},{"key":"10.1016\/j.patrec.2016.03.031_bib0025","series-title":"Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)","first-page":"572","article-title":"Matrix coherence and the Nystrom method","author":"Talwalkar","year":"2010"},{"key":"10.1016\/j.patrec.2016.03.031_bib0026","series-title":"Proceedings of the 26th Annual International Conference on Machine Learning, ICML\u201909","first-page":"1113","article-title":"Feature hashing for large scale multitask learning","author":"Weinberger","year":"2009"},{"key":"10.1016\/j.patrec.2016.03.031_bib0027","series-title":"Advances in Neural Information Processing Systems 13: Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA","first-page":"682","article-title":"Using the Nystr\u00f6m method to speed up kernel machines","author":"Williams","year":"2000"}],"container-title":["Pattern Recognition Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865516300356?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865516300356?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2018,9,13]],"date-time":"2018-09-13T09:22:10Z","timestamp":1536830530000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167865516300356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7]]},"references-count":27,"alternative-id":["S0167865516300356"],"URL":"https:\/\/doi.org\/10.1016\/j.patrec.2016.03.031","relation":{},"ISSN":["0167-8655"],"issn-type":[{"value":"0167-8655","type":"print"}],"subject":[],"published":{"date-parts":[[2016,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Kernel matrix decomposition via empirical kernel map","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patrec.2016.03.031","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2016 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}