{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T19:24:23Z","timestamp":1762543463503,"version":"3.41.2"},"reference-count":38,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"DOI":"10.13039\/501100001809","name":"NSF of China","doi-asserted-by":"crossref","award":["61602148","11401163"],"award-info":[{"award-number":["61602148","11401163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Project of Hebei Education Department","award":["ZD2021043","QN2018126","BJ2020011","ZD2021319"],"award-info":[{"award-number":["ZD2021043","QN2018126","BJ2020011","ZD2021319"]}]},{"name":"Hebei University of Economics and Business Foundation","award":["2020YB13","2019PY01","2020YB01","2015KYQ07"],"award-info":[{"award-number":["2020YB13","2019PY01","2020YB01","2015KYQ07"]}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"crossref","award":["F2017207010","F2019207118"],"award-info":[{"award-number":["F2017207010","F2019207118"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:p> Metric learning is a critical problem in classification. Most classifiers are based on a metric, the simplest one is the KNN classifier, whose outcome is directly decided by the given metric. This paper will discuss semi-supervised metric learning. Most traditional semi-supervised metric learning algorithms preserve the local structure of all the samples (including labeled and unlabeled) in the input space, when making the same labeled samples together and separating different labeled samples. In most existing methods, the local structure is calculated by the Euclidean distance which uses all the features. As we all know, high dimensional data lies on a low dimension manifold, and not all the features are discriminative. Thus, in this paper, we try to explore the latent structure of the samples and use the more discriminative features to calculate the local structure. The latent structure is learned by clustering random forest and cast into similarity between samples. Based on the hierarchical structure of the trees and the split function, the similarity is obtained from discriminant features. Experimental results on public data sets show our algorithm outperforms the traditional similar related algorithms. <\/jats:p>","DOI":"10.1142\/s1469026821500139","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T13:21:38Z","timestamp":1623158498000},"source":"Crossref","is-referenced-by-count":2,"title":["Regularized Semi-Supervised Metric Learning with Latent Structure Preserved"],"prefix":"10.1142","volume":"20","author":[{"given":"Qianying","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mathematics and Statistics, Hebei University of Economics and Business, Shijiazhuang 050000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Statistics, Hebei University of Economics and Business, Shijiazhuang 050000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Mathematics and Statistics, Hebei University of Economics and Business, Shijiazhuang 050000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"S1469026821500139BIB001","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0057"},{"key":"S1469026821500139BIB002","first-page":"539","volume":"1","author":"Sumit C.","year":"2005","journal-title":"IEEE Comput. Soc. Conf. Computer Vision Pattern Recogn."},{"key":"S1469026821500139BIB003","first-page":"709","volume-title":"Asian Conference on Computer Vision","author":"Nguyen H. V.","year":"2010"},{"key":"S1469026821500139BIB004","doi-asserted-by":"publisher","DOI":"10.1145\/1823746.1823752"},{"key":"S1469026821500139BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2007.4408839"},{"key":"S1469026821500139BIB006","doi-asserted-by":"publisher","DOI":"10.1007\/11503415_8"},{"key":"S1469026821500139BIB007","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"S1469026821500139BIB008","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-27819-1_43"},{"key":"S1469026821500139BIB009","first-page":"57","volume-title":"Int. Workshop Artificial Intelligence and Statistics","author":"Chapelle O.","year":"2004"},{"key":"S1469026821500139BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2009.2035866"},{"key":"S1469026821500139BIB011","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2007.4408856"},{"key":"S1469026821500139BIB012","doi-asserted-by":"publisher","DOI":"10.1142\/S1469026818500116"},{"key":"S1469026821500139BIB013","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015391"},{"key":"S1469026821500139BIB014","first-page":"505","volume":"15","author":"Xing E. P.","year":"2002","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"S1469026821500139BIB015","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-39351-3"},{"volume-title":"Pattern Classification","year":"2012","author":"Duda R. O.","key":"S1469026821500139BIB016"},{"key":"S1469026821500139BIB017","volume-title":"Multidimensional Scaling","volume":"88","author":"Cox T. F.","year":"2001"},{"key":"S1469026821500139BIB018","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"S1469026821500139BIB019","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"volume-title":"Introduction to Statistical Pattern Recognition","year":"1990","author":"Fukunaga K.","key":"S1469026821500139BIB020"},{"key":"S1469026821500139BIB021","first-page":"13","author":"Goldberger J.","year":"2004","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"S1469026821500139BIB022","first-page":"451","volume":"18","author":"Globerson A.","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"41","key":"S1469026821500139BIB023","volume":"16","author":"Schultz M.","year":"2004","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"S1469026821500139BIB024","first-page":"577","volume-title":"Proc. 18th Int. Conf. Machine Learning","author":"Wagstaff K.","year":"2001"},{"key":"S1469026821500139BIB025","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.05.018"},{"key":"S1469026821500139BIB026","first-page":"1217","volume-title":"Proc. 21st Int. Joint Conf. Artifical Intelligence","author":"Baghshah M. S.","year":"2009"},{"key":"S1469026821500139BIB027","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.02.015"},{"key":"S1469026821500139BIB028","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.883723"},{"issue":"1","key":"S1469026821500139BIB029","first-page":"207","volume":"10","author":"Weinberger K. Q.","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"S1469026821500139BIB030","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354775"},{"issue":"3","key":"S1469026821500139BIB031","first-page":"582","volume":"40","author":"Breiman L. I.","year":"1984","journal-title":"Boimetrics"},{"key":"S1469026821500139BIB032","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"S1469026821500139BIB033","first-page":"1450","author":"Zhu X.","year":"2014","journal-title":"Comput. Vision Pattern Recognit."},{"key":"S1469026821500139BIB034","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000001230"},{"key":"S1469026821500139BIB035","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835947"},{"key":"S1469026821500139BIB037","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"S1469026821500139BIB038","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"S1469026821500139BIB039","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126477"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026821500139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:16:51Z","timestamp":1665371811000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S1469026821500139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":38,"journal-issue":{"issue":"02","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["10.1142\/S1469026821500139"],"URL":"https:\/\/doi.org\/10.1142\/s1469026821500139","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"type":"print","value":"1469-0268"},{"type":"electronic","value":"1757-5885"}],"subject":[],"published":{"date-parts":[[2021,6]]},"article-number":"2150013"}}