文献类型：期刊文献

英文题名：Global and local multi-view multi-label learning

作者：Zhu, Changming[1,2];Miao, Duoqian[2];Wang, Zhe[3];Zhou, Rigui[1];Wei, Lai[1];Zhang, Xiafen[1]

机构：[1]Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China;[2]Tongji Univ, Coll Elect & Informat Engn, Shanghai 200092, Peoples R China;[3]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：371

起止页码：67

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20193807451686);WOS:【SCI-EXPANDED(收录号:WOS:000493950600006)】；

基金：This work is sponsored by 'Chenguang Program' supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under grant number 18CG54. Furthermore, this work is also supported by National Natural Science Foundation of China (CN) under grant number 61602296, Natural Science Foundation of Shanghai (CN) under grant number 16ZR1414500, Project funded by China Postdoctoral Science Foundation under grant number 2019M651576, and the authors would like to thank their supports.

语种：英文

外文关键词：Multi-label; Label correlation; Multi-view

摘要：In order to process multi-view multi-label data sets, we propose global and local multi-view multi-label learning (GLMVML). This method can exploit global and local label correlations of both the whole data set and each view simultaneously. What's more, GLMVML introduces a consensus multi-view representation which encodes the complementary information from different views. Related experiments on three multi-view data sets, fourteen multi-label data sets, and one multi-view multi-label data set have validated that (1) GLMVML has a better average AUC and precision and it is superior to the classical multi-view learning methods and multi-label learning methods in statistical; (2) the running time of GLMVML won't add too much; (3) GLMVML has a good convergence and ability to process multi-view multi-label data sets; (4) since the model of GLMVML consists of both the global label correlations and local label correlations, so parameter values should be moderate rather than too large or too small. (C) 2019 Elsevier B.V. All rights reserved.