基本追求
压缩传感
矩阵完成
矩阵范数
算法
基质(化学分析)
基础(线性代数)
估计员
数学优化
计算机科学
规范(哲学)
缩小
凸优化
功能(生物学)
数学
正多边形
材料科学
法学
复合材料
生物
几何学
量子力学
进化生物学
政治学
高斯分布
特征向量
物理
统计
匹配追踪
作者
Stéphane Gaı̈ffas,Guillaume Lecué
出处
期刊:Cornell University - arXiv
日期:2011-01-01
被引量:11
标识
DOI:10.48550/arxiv.1107.1638
摘要
This paper is about iteratively reweighted basis-pursuit algorithms for compressed sensing and matrix completion problems. In a first part, we give a theoretical explanation of the fact that reweighted basis pursuit can improve a lot upon basis pursuit for exact recovery in compressed sensing. We exhibit a condition that links the accuracy of the weights to the RIP and incoherency constants, which ensures exact recovery. In a second part, we introduce a new algorithm for matrix completion, based on the idea of iterative reweighting. Since a weighted nuclear "norm" is typically non-convex, it cannot be used easily as an objective function. So, we define a new estimator based on a fixed-point equation. We give empirical evidences of the fact that this new algorithm leads to strong improvements over nuclear norm minimization on simulated and real matrix completion problems.
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