增广拉格朗日法
矩阵完成
奇异值分解
矩阵分解
基质(化学分析)
秩(图论)
矩阵范数
低秩近似
数学优化
稳健主成分分析
稀疏矩阵
奇异值
应用数学
计算机科学
数学
算法
张量(固有定义)
人工智能
特征向量
主成分分析
组合数学
物理
复合材料
高斯分布
材料科学
纯数学
量子力学
作者
Yonghua Shen,Zhen Wen,Y. Zhang
标识
DOI:10.1080/10556788.2012.700713
摘要
The matrix separation problem aims to separate a low-rank matrix and a sparse matrix from their sum. This problem has recently attracted considerable research attention due to its wide range of potential applications. Nuclear-norm minimization models have been proposed for matrix separation and proved to yield exact separations under suitable conditions. These models, however, typically require the calculation of a full or partial singular value decomposition at every iteration that can become increasingly costly as matrix dimensions and rank grow. To improve scalability, in this paper, we propose and investigate an alternative approach based on solving a non-convex, low-rank factorization model by an augmented Lagrangian alternating direction method. Numerical studies indicate that the effectiveness of the proposed model is limited to problems where the sparse matrix does not dominate the low-rank one in magnitude, though this limitation can be alleviated by certain data pre-processing techniques. On the other hand, extensive numerical results show that, within its applicability range, the proposed method in general has a much faster solution speed than nuclear-norm minimization algorithms and often provides better recoverability.
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