{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T22:47:27Z","timestamp":1649026047699},"reference-count":26,"publisher":"Hindawi Limited","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":["61403394","71573256","2014QNA46"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2016]]},"abstract":"Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.<\/jats:p>","DOI":"10.1155\/2016\/4920670","type":"journal-article","created":{"date-parts":[[2016,5,9]],"date-time":"2016-05-09T21:03:03Z","timestamp":1462827783000},"page":"1-12","source":"Crossref","is-referenced-by-count":1,"title":["Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing"],"prefix":"10.1155","volume":"2016","author":[{"given":"Shuang","family":"Li","sequence":"first","affiliation":[{"name":"School of Management, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China"}]}],"member":"98","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.03.012"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.12.001"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2010.89"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2215617"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.07.018"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.02.007"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-1904-8"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"11","first-page":"585","volume-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","year":"2001"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2007.250598"},{"key":"14","volume-title":"Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering","year":"2003"},{"key":"16","first-page":"705","volume-title":"Global versus local methods in nonlinear dimensionality reduction","year":"2003"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.09.073"},{"key":"19","first-page":"396","volume-title":"Learning non-linear combinations of kernels","volume":"22","year":"2009"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2010.183"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2013.09.004"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.03.019"},{"key":"26","first-page":"2399","volume":"7","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2011.2162000"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1137\/120864799"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.05.018"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.09.002"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2007.190669"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.3390\/s121013694"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/4920670.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/4920670.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/4920670.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T20:41:06Z","timestamp":1589661666000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cin\/2016\/4920670\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":26,"alternative-id":["4920670","4920670"],"URL":"https:\/\/doi.org\/10.1155\/2016\/4920670","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}