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Learning a Coupled Linearized Method in Online Setting
Xue, Wei1; Zhang, Wensheng1,2
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2017-02-01
Volume28Issue:2Pages:438-450
SubtypeArticle
AbstractBased on the alternating direction method of multipliers, in this paper, we propose, analyze, and test a coupled linearized method, which aims to minimize an unconstrained problem consisting of a loss term and a regularization term in an online setting. To solve this problem, we first transform it into an equivalent constrained minimization problem with a separable structure. Then, we split the corresponding augmented Lagrangian function and minimize the resulting subproblems distributedly with one variable by fixing another one. This method is easy to execute without calculating matrix inversion by implementing three linearized operations per iteration, and at each iteration, we can obtain a closed-form solution. In particular, our update rule contains the well-known soft-thresholding operator as a special case. Moreover, upper bound on the regret of the proposed method is analyzed. Under some mild conditions, it can achieve 0(1/ root T) convergence rate for convex learning problems and 0((logT)/ T) for strongly convex learning. Numerical experiments and comparisons with several state-of-the-art methods are reported, which demonstrate the efficiency and effectiveness of our approach.
KeywordAlternating Minimization Convex Optimization Linearized Operation Online Learning Regret Bound
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2514413
WOS KeywordOPTIMIZATION METHODS ; ALGORITHM ; REGRESSION ; SHRINKAGE ; SELECTION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(U1135005 ; Project of Post-Graduate Scientific Research Innovation Program of Jiangsu Province(KYZZ15_0123) ; 61305018 ; 61432008 ; 61472423 ; 61532006)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000394522900017
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14383
Collection精密感知与控制研究中心_人工智能与机器学习
Affiliation1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xue, Wei,Zhang, Wensheng. Learning a Coupled Linearized Method in Online Setting[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(2):438-450.
APA Xue, Wei,&Zhang, Wensheng.(2017).Learning a Coupled Linearized Method in Online Setting.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(2),438-450.
MLA Xue, Wei,et al."Learning a Coupled Linearized Method in Online Setting".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.2(2017):438-450.
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