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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 |
DOI | 10.1109/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 |
URL | 查看原文 |
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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