文献类型：期刊文献

英文题名：Coupling loss and self-used privileged information guided multi-view transfer learning

作者：Tang, Jingjing[1,2];He, Yiwei[3];Tian, Yingjie[4,5,6];Liu, Dalian[7,9];Kou, Gang[1,2];Alsaadi, Fawaz E.[8]

第一作者：Tang, Jingjing

通讯作者：Tian, YJ[1];Liu, DL[2]

机构：[1]Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 611130, Peoples R China;[2]Southwestern Univ Finance & Econ, Inst Big Data, Chengdu 611130, Peoples R China;[3]Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China;[4]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[5]Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[6]Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China;[7]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China;[8]King Abdulaziz Univ, Fac Comp & IT, Dept Informat Technol, Jeddah, Saudi Arabia;[9]Beijing Union Univ, Inst Fundamental & Interdisciplinary Sci, Beijing 100101, Peoples R China

第一机构：Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 611130, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[2]corresponding author), Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China.|[1141788]北京联合大学基础教学部;[11417]北京联合大学;

年份：2021

卷号：551

起止页码：245-269

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20204909586194);Scopus(收录号：2-s2.0-85097060456);WOS:【SCI-EXPANDED(收录号:WOS:000612172900015)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 71901179, 71731009, 61472390, 71331005, 91546201, 71725001, 71471149, 71271191, 71991472, 71910107002 and 71950010), the Beijing Natural Science Foundation (No.1162005), Premium Funding Project for Academic Human Resources Development in Beijing Union University, and Major project of the National Social Science Foundation of China (No. 15ZDB153).

语种：英文

外文关键词：Transfer learning; Multi-view learning; Support vector machine; Privileged information; Coupling loss

摘要：Transfer learning builds models for the target domain by leveraging the information from another related source domain, in which the distributions of two domains are usually quite distinct. Real-world data are often characterized by multiple representations known as multi-view features. In the multi-view transfer learning field, existing methods aim to address the following two issues. Firstly, due to the distributional difference between the two domains, the classifier trained on the source domain may underperform on the target domain. Moreover, the lack of data from the target domain generally occurs in the training phase. Secondly, how to fully exploit the relations among multiple features is challenging when such multi-view representations emerge in the source and target domains. In this paper, we propose a new coupling loss and self-used privileged information guided multi-view transfer learning method (MVTL-CP). The first issue is addressed by utilizing the weighted labeled data from the source domain to learn a precise classifier for the target domain. Following the consensus and complementarity principles, we tackle the second issue by making the best use of multiple views. Furthermore, we analyze the consistency between views and the generalization capability of MVTL-CP. Comprehensive experiments confirm the effectiveness of our proposed model. (C) 2020 Elsevier Inc. All rights reserved.