"Low-rank plus dual" model based dimensionality reduction

被引:2
|
作者
Wang, Si-Qi [1 ]
Feng, Xiang-Chu [1 ]
Wang, Wei-Wei [1 ]
机构
[1] Xidian Univ, Sch Math & Stat, Xian 710126, Peoples R China
基金
美国国家科学基金会;
关键词
Dimensionality reduction; Background modeling; Singular value decomposition; Thresholding method; l(p)-Minimization problem; MATRIX; ALGORITHM; NOISE;
D O I
10.1016/j.neucom.2015.07.117
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a novel "low-rank + dual" model for the matrix decomposition problems. Based on the unitarily invariant property of the Schatten p-norm, we prove that the solution of the proposed model can be obtained by an "l(infinity) + l(1)" minimization problem, thus a simple and fast algorithm can be provided to solve our new model. Furthermore, we find that applying "l(infinity) + l(1)" to any vector can achieve a shifty threshold on the values. Experiments on the simulation data, the real surveillance video database and the Yale B database prove the proposed method to outperform the state-of-the-art techniques. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:3 / 10
页数:8
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