Generalized Low-Rank Matrix Completion via Nonconvex Schatten p-Norm Minimization
Wu, Qiong; Zhang, Fan; Wang, Hao; Shi, Yuanming
2018
会议录名称2018 IEEE 88TH VEHICULAR TECHNOLOGY CONFERENCE (VTC-FALL)
卷号2018-August
发表状态已发表
DOI10.1109/VTCFall.2018.8690953
摘要In this paper, we present a generalized low-rank matrix completion (LRMC) model for topological interference management (TIM), thereby maximizing the achievable degrees of freedom (DoFs) only based on the network connectivity information. Unfortunately, contemporary convex relaxation approaches, e.g, nuclear norm minimization, fail to return low rank solutions, due to the poor structures in the generalized low rank model. Most existing nonconvex approaches, however, often need the optimal rank as prior information, which is unavailable in our setting. We thus propose a novel nonconvex relaxation approach with the nonconvex Schatten p-norm to provide a tight approximation for the rank function. A smooth function is formulated to approximate the nonsmooth and nonconvex objective, then an Iteratively Reweighted Least Squares (IRLS-p) method is employed to handle the nonconvexity of the model, which iteratively minimizes the weighted Frobenius norm models of smoothed subproblems while driving the smoothing parameter to 0. We further improve the efficiency by proposing an Iteratively Adaptively Reweighted Least Squares (IARLS-p) algorithm, which uses an adaptively updating strategy for the smoothing parameters in each iteration. Numerical results exhibit the ability of the proposed algorithm to find low-rank solutions, that is, it can achieve higher DoFs in most cases.
会议地点Chicago, IL, USA
会议日期27-30 Aug. 2018
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收录类别EI ; CPCI-S ; CPCI
语种英语
WOS研究方向Transportation
WOS类目Transportation Science & Technology
WOS记录号WOS:000468872400401
出版者IEEE
EI入藏号20191806858618
EI主题词Degrees of freedom (mechanics) ; Information management ; Least squares approximations ; Relaxation processes ; Topology
EI分类号Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Numerical Methods:921.6 ; Mechanics:931.1
WOS关键词SPARSE
原始文献类型Proceedings Paper
引用统计
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/34269
专题信息科学与技术学院_硕士生
信息科学与技术学院_PI研究组_王浩组
信息科学与技术学院_PI研究组_石远明组
通讯作者Wu, Qiong
作者单位ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
第一作者单位信息科学与技术学院
通讯作者单位信息科学与技术学院
第一作者的第一单位信息科学与技术学院
推荐引用方式
GB/T 7714
Wu, Qiong,Zhang, Fan,Wang, Hao,et al. Generalized Low-Rank Matrix Completion via Nonconvex Schatten p-Norm Minimization[C]:IEEE,2018.
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