Robust tensor analysis with L1-norm

Yanwei Pang; Xuelong Li; Yuan Yuan

doi:10.1109/TCSVT.2009.2020337

Robust tensor analysis with L1-norm

Yanwei Pang, Xuelong Li, Yuan Yuan

科研成果: 期刊稿件 › 文章 › 同行评审

193 引用（Scopus）

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.

源语言	英语
文章编号	4812108
页（从-至）	172-178
页数	7
期刊	IEEE Transactions on Circuits and Systems for Video Technology
卷	20
期	2
DOI	https://doi.org/10.1109/TCSVT.2009.2020337
出版状态	已出版 - 2月 2010
已对外发布	是

访问文件

10.1109/TCSVT.2009.2020337

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{3b399558710f418499740956a7233d56,

title = "Robust tensor analysis with L1-norm",

abstract = "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.",

keywords = "L1-norm, Outlier, Tensor analysis",

author = "Yanwei Pang and Xuelong Li and Yuan Yuan",

year = "2010",

month = feb,

doi = "10.1109/TCSVT.2009.2020337",

language = "英语",

volume = "20",

pages = "172--178",

journal = "IEEE Transactions on Circuits and Systems for Video Technology",

issn = "1051-8215",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

TY - JOUR

T1 - Robust tensor analysis with L1-norm

AU - Pang, Yanwei

AU - Li, Xuelong

AU - Yuan, Yuan

PY - 2010/2

Y1 - 2010/2

N2 - 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.

AB - 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.

KW - L1-norm

KW - Outlier

KW - Tensor analysis

UR - http://www.scopus.com/inward/record.url?scp=76649139002&partnerID=8YFLogxK

U2 - 10.1109/TCSVT.2009.2020337

DO - 10.1109/TCSVT.2009.2020337

M3 - 文章

AN - SCOPUS:76649139002

SN - 1051-8215

VL - 20

SP - 172

EP - 178

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

IS - 2

M1 - 4812108

ER -

Robust tensor analysis with L1-norm

摘要

访问文件

其它文件与链接

指纹

引用此