{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:15:03Z","timestamp":1758636903904,"version":"3.37.3"},"reference-count":29,"publisher":"Wiley","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572399","2013KJXX-29"],"award-info":[{"award-number":["61572399","2013KJXX-29"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Provincial Youth Science and Technology Star Plan","award":["61572399","2013KJXX-29"],"award-info":[{"award-number":["61572399","2013KJXX-29"]}]},{"name":"New Star Team of Xi\u2019an University of Posts & Telecommunications","award":["61572399","2013KJXX-29"],"award-info":[{"award-number":["61572399","2013KJXX-29"]}]},{"name":"Provincial Key Disciplines Construction Fund of General Institutions of Higher Education in Shaanxi","award":["61572399","2013KJXX-29"],"award-info":[{"award-number":["61572399","2013KJXX-29"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn mathvariant=\"normal\">1<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>the projection vectors for dimension reduction,<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn fontstyle=\"italic\">2<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>the input weights, biases, and output weights, and<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mn fontstyle=\"italic\">3<\/mml:mn><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.<\/jats:p>","DOI":"10.1155\/2016\/5197932","type":"journal-article","created":{"date-parts":[[2016,4,8]],"date-time":"2016-04-08T07:31:43Z","timestamp":1460100703000},"page":"1-13","source":"Crossref","is-referenced-by-count":1,"title":["Online Sequential Projection Vector Machine with Adaptive Data Mean Update"],"prefix":"10.1155","volume":"2016","author":[{"given":"Lin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Ji-Ting","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Qiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Wan-Yu","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xian University of Technology, Xi\u2019an 710048, China"}]}],"member":"311","reference":[{"key":"24","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.2.213"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1993.5.6.954"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.2.461"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2004.834428"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2004.836241"},{"first-page":"1","volume-title":"Learning via gaussian herding","year":"2010","key":"25"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-012-5319-2"},{"issue":"1","key":"2","first-page":"3103","volume":"13","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-010-0420-4"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2006.880583"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17432-2_14"},{"year":"2007","key":"19"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tsmcb.2011.2168604"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.3390\/s110504794"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1002\/dac.2522"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-47969-4_47"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/S0262-8856(02)00114-2"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/83.855432"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2009.2036259"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2007.07.025"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2007.10.008"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1504\/IJCBDD.2010.035238"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2007.2000059"},{"key":"30","first-page":"3371","volume":"11","year":"2010","journal-title":"Journal of Machine Learning Research"},{"year":"2006","series-title":"Duxbury Advanced","key":"34"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1002\/wics.101"},{"year":"2002","series-title":"Springer Series in Statistics","key":"36"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/5197932.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/5197932.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/5197932.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,8,18]],"date-time":"2016-08-18T16:37:02Z","timestamp":1471538222000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cin\/2016\/5197932\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":29,"alternative-id":["5197932","5197932"],"URL":"https:\/\/doi.org\/10.1155\/2016\/5197932","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2016]]}}}