文献类型：期刊文献

英文题名：Multi-view generalized support vector machine via mining the inherent relationship between views with applications to face and fire smoke recognition

作者：Cheng, Yawen[1,2] Fu, Liyong[3] Luo, Peng[3] Ye, Qiaolin[1,2] Liu, Fan[4] Zhu, Wei[1]

第一作者：Cheng, Yawen

通信作者：Ye, QL[1]

机构：[1]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Jiangsu, Peoples R China;[2]Huaiyin Inst Technol, Lab Internet Things & Mobile Internet Technol Jia, Nanjing 223003, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[4]Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China

年份：2020

卷号：210

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20204109337416);Scopus(收录号：2-s2.0-85092244313);WOS:【SCI-EXPANDED(收录号:WOS:000600971700004)】；

基金：This work was supported in part by the Central Public Interest Scientific Institution Basal Research Fund under Grant CAFYBB2017ZC001, the Natural Science Foundation of Jiangsu Province under Grant BK20171453, the National Science Foundations of China under Grant 62072246, and 61773210, and Qinglan and Six Talent Peaks Projects of Jiangsu Province.

语种：英文

外文关键词：Multi-view learning; GEPSVM; Co-regularization

摘要：Multiview Generalized Eigenvalue Proximal Support Vector Machines (MvGSVMs) is an effective multi view classification algorithm, which effectively combines multi-view learning and classification. Then it was found that in the classification learning task, the classifier combined with multi-view learning has a better classification effect than considering only a single view. In order to utilize the multi view learning framework more fully and accurately, we further research this. We explore the internal relationship between different views between samples to replace the method of connecting different views through distance combinations. We propose a new method named Multi-view Generalized Support Vector Machine via Mining the Inherent Relationship between Views (MRMvGSVM). At the same time, we use the L2,1-norm constraint relationship matrix as a multi-view regularization term to select the most relevant sample data from different views. It not only helps to improve the accuracy of classification but also reduces the influence of extraneous factors to a certain extent and improves the robustness of the algorithm. The effectiveness of the algorithm is proved by theory and experiments on UCI, and Face and Fire Smoke image datasets. (C) 2020 Elsevier B.V. All rights reserved.