Robust Tensor Analysis With L1-Norm

被引：163
|
作者：
Pang, Yanwei [1 ]
Li, Xuelong [2 ]
Yuan, Yuan [3 ]
机构：
[1] Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China
[2] Chinese Acad Sci, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
[3] Aston Univ, Sch Engn & Appl Sci, Birmingham B4 7ET, W Midlands, England
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY | 2010年 / 20卷 / 02期
基金：
中国国家自然科学基金;
关键词：
L1-norm; outlier; tensor analysis; APPEARANCE;
D O I：
10.1109/TCSVT.2009.2020337
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Tensor analysis plays an important role in modern image and vision computing problems. Most of the existing tensor analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based tensor analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.
引用
收藏
页码：172 / 178
页数：7
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