Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction

doi:10.1109/TPAMI.2020.2991344

	Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction
	Yang, Zhiyong 1,2; Xu, Qianqian 3; Cao, Xiaochun 1,2,4; Huang, Qingming 3,5,6,7
	2021-11-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	43 期号:11 页码:4094-4110
摘要	As an effective learning paradigm against insufficient training samples, multi-task learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called task-feature collaborative learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method.
关键词	Task analysis Convergence Predictive models Diseases Collaborative work Optimization Training Block diagonal structural learning negative transfer multi-task learning global convergence
DOI	10.1109/TPAMI.2020.2991344
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0102003] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[U1736219] ; National Natural Science Foundation of China[61971016] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61672514] ; National Natural Science Foundation of China[61976202] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000] ; Beijing Natural Science Foundation[4182079] ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000702649700028
出版者	IEEE COMPUTER SOC
引用统计	被引频次：5[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/2XEOYT63/17040
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yang, Zhiyong
作者单位	1.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Cyberspace Secur Res Ctr, Peng Cheng Lab, Shenzhen 518055, Peoples R China 5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 6.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China 7.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Yang, Zhiyong,Xu, Qianqian,Cao, Xiaochun,et al. Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(11):4094-4110.
APA	Yang, Zhiyong,Xu, Qianqian,Cao, Xiaochun,&Huang, Qingming.(2021).Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(11),4094-4110.
MLA	Yang, Zhiyong,et al."Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.11(2021):4094-4110.