文献类型：期刊文献

英文题名：Multi-view transfer learning with privileged learning framework

作者：He, Yiwei[1];Tian, Yingjie[2,3,4];Liu, Dalian[5]

第一作者：He, Yiwei

通讯作者：Tian, YJ[1];Liu, DL[2]

机构：[1]Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China;[2]Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100090, Peoples R China;[3]Chinese Aacdemy Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[4]Chinese Aacdemy Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[5]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China

第一机构：Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China

通讯机构：[1]corresponding author), Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100090, 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：2019

卷号：335

起止页码：131-142

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20190506445253);Scopus(收录号：2-s2.0-85060576231);WOS:【SCI-EXPANDED(收录号:WOS:000459130600012)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 71731009, 61472390, 71331005 and 91546201), the Beijing Natural Science Foundation (No. 1162005) and Premium Funding Project for Academic Human Resources Development in Beijing Union University.

语种：英文

外文关键词：Multi-view learning; Transfer learning; Learning using privileged information; Support vector machine

摘要：In this paper, we present a multi-view transfer learning model named Multi-view Transfer Discriminative Model (MTDM) for both image and text classification tasks. Transfer learning, which aims to learn a robust classifier for the target domain using data from a different distribution, has been proved to be effective in many real-world applications. However, most of the existing transfer learning methods map across domain data into a high-dimension space which the distance between domains is closed. This strategy always fails in the multi-view scenario. On the contrary, the multi-view learning methods are also difficult to extend in the transfer learning settings. One of our goals in this paper is to develop a model which can perform better in both multi-view and transfer learning settings. On the one hand, the problem of multi-view is implemented by the paradigm of learning using privileged information (LUPI), which could guarantee the principle of complementary and consensus. On the other hand, the model adequately utilizes the source domain data to build a robust classifier for the target domain. We evaluate our model on both image and text classification tasks and show the effectiveness compared with other baseline approaches. (C) 2019 Elsevier B.V. All rights reserved.