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Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nystr\u00f6m method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.<\/jats:p>","DOI":"10.1155\/2017\/4629534","type":"journal-article","created":{"date-parts":[[2017,2,13]],"date-time":"2017-02-13T16:00:41Z","timestamp":1487001641000},"page":"1-9","source":"Crossref","is-referenced-by-count":11,"title":["Ranking Support Vector Machine with Kernel Approximation"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4160-1024","authenticated-orcid":true,"given":"Kai","family":"Chen","sequence":"first","affiliation":[{"name":"National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-7961","authenticated-orcid":true,"given":"Rongchun","family":"Li","sequence":"additional","affiliation":[{"name":"National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Dou","sequence":"additional","affiliation":[{"name":"National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7514-4250","authenticated-orcid":true,"given":"Zhengfa","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4115-720X","authenticated-orcid":true,"given":"Qi","family":"Lv","sequence":"additional","affiliation":[{"name":"National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1561\/1500000016"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2016.01.004"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-009-9109-9"},{"key":"11","first-page":"2153","volume":"6","year":"2005","journal-title":"Journal of Machine Learning Research"},{"key":"12","first-page":"682","volume-title":"Using the Nystr\u00f6m method to speed up Kernel machines","year":"2001"},{"key":"13","first-page":"1177","volume-title":"Random features for large-scale kernel machines","year":"2007"},{"key":"14","first-page":"1313","volume-title":"Weighted sums of random kitchen sinks: replacing minimization with randomization in learning","year":"2008"},{"key":"15","year":"2003"},{"key":"16","first-page":"897","volume-title":"McRank: learning to rank using multiple classification and gradient boosting","year":"2007"},{"key":"17","first-page":"937","volume-title":"Ranking with large margin principle: two approaches","year":"2002"},{"issue":"6","key":"18","first-page":"933","volume":"4","year":"2003","journal-title":"The Journal of Machine Learning Research"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-92910-9_15"},{"key":"24","first-page":"485","volume-title":"Nystr\u00f6m method vs random Fourier features: a theoretical and empirical comparison","year":"2012"},{"key":"26","volume":"463","year":"1999"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2017\/4629534.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2017\/4629534.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2017\/4629534.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,2,13]],"date-time":"2017-02-13T16:00:44Z","timestamp":1487001644000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2017\/4629534\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":14,"alternative-id":["4629534","4629534"],"URL":"https:\/\/doi.org\/10.1155\/2017\/4629534","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}