Supervised representation learning for multi-label classification

doi:10.1007/s10994-019-05783-5

	Supervised representation learning for multi-label classification
	Huang, Ming 1,2; Zhuang, Fuzhen 1,2; Zhang, Xiao 3; Ao, Xiang 1,2; Niu, Zhengyu 4; Zhang, Min-Ling 5; He, Qing 1,2
	2019-05-01
发表期刊	MACHINE LEARNING
ISSN	0885-6125
卷号	108 期号:5 页码:747-763
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.
关键词	Representation learning Multi-label learning Two-encoding-layer autoencoder
DOI	10.1007/s10994-019-05783-5
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000470185100003
出版者	SPRINGER
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4202
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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China 4.Baidu Inc, Beijing, Peoples R China 5.South East Univ, Nanjing, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Huang, Ming,Zhuang, Fuzhen,Zhang, Xiao,et al. Supervised representation learning for multi-label classification[J]. MACHINE LEARNING,2019,108(5):747-763.
APA	Huang, Ming.,Zhuang, Fuzhen.,Zhang, Xiao.,Ao, Xiang.,Niu, Zhengyu.,...&He, Qing.(2019).Supervised representation learning for multi-label classification.MACHINE LEARNING,108(5),747-763.
MLA	Huang, Ming,et al."Supervised representation learning for multi-label classification".MACHINE LEARNING 108.5(2019):747-763.