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Supervised representation learning for multi-label classification

文献类型：期刊论文

作者Niu, Zhengyu2; Huang, Ming4,5; Zhuang, Fuzhen4,5; Zhang, Xiao3; Ao, Xiang4,5; Zhang, Min-Ling1; He, Qing4,5
刊名MACHINE LEARNING
出版日期2019-05-01
卷号108期号:5页码:747-763
关键词Representation learning Multi-label learning Two-encoding-layer autoencoder
ISSN号0885-6125
DOI10.1007/s10994-019-05783-5
英文摘要Representation learning is one of the most important aspects of multi-label learning because of the intricate nature of multi-label data. Current research on representation learning either fails to consider label knowledge or is affected by the lack of labeled data. Moreover, most of them learn the representations and incorporate the label information in a two-step manner. In this paper, due to the success of representation learning by deep learning we propose a novel framework based on neural networks named SERL to learn global feature representation by jointly considering all labels in an effective supervised manner. At its core, a two-encoding-layer autoencoder, which can utilize labeled and unlabeled data, is adopted to learn feature representation in the supervision of softmax regression. Specifically, the softmax regression incorporates label knowledge to improve the performance of both representation learning and multi-label learning by being jointly optimized with the autoencoder. Moreover, the autoencoder is expanded into two encoding layers to share knowledge with the softmax regression by sharing the second encoding weight matrix. We conduct extensive experiments on five real-world datasets to demonstrate the superiority of SERL over other state-of-the-art multi-label learning approaches.
资助项目National Key Research and Development Program of China[2018YFB1004300] ; National Natural Science Foundation of China[61773361] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[U1811461] ; Project of Youth Innovation Promotion Association CAS[2017146]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000470185100003
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/4201]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhuang, Fuzhen
作者单位1.South East Univ, Nanjing, Jiangsu, Peoples R China
2.Baidu Inc, Beijing, Peoples R China
3.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Niu, Zhengyu,Huang, Ming,Zhuang, Fuzhen,et al. Supervised representation learning for multi-label classification[J]. MACHINE LEARNING,2019,108(5):747-763.
APA Niu, Zhengyu.,Huang, Ming.,Zhuang, Fuzhen.,Zhang, Xiao.,Ao, Xiang.,...&He, Qing.(2019).Supervised representation learning for multi-label classification.MACHINE LEARNING,108(5),747-763.
MLA Niu, Zhengyu,et al."Supervised representation learning for multi-label classification".MACHINE LEARNING 108.5(2019):747-763.

入库方式： OAI收割

来源：计算技术研究所

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