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In this study, by considering a common hidden space cross all the views, consistent hidden density distribution between views in the common hidden space is delved so as to address this issue. Accordingly, a novel multi-view support vector machine based on consistent hidden density distributions between views in common hidden space (2V-SVM-CHDD) is proposed for an efficient multi-view face recognition, and its theoretical convergence is also analyzed. Extensive experimental results on real face image datasets indicate the effectiveness of the proposed multi-view method.<\/jats:p>","DOI":"10.3233\/jifs-181048","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T10:52:31Z","timestamp":1558695151000},"page":"5245-5259","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A novel multi-view SVM based on consistent hidden density distributions between views for face recognition"],"prefix":"10.1177","volume":"36","author":[{"given":"Zhibin","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Digital Media, Jiangnan University, Wuxi, Jiangsu, P.R. China"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Digital Media, Jiangnan University, Wuxi, Jiangsu, P.R. 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