L1-norm-based 2DPCA

Xuelong Li; Yanwei Pang; Yuan Yuan

doi:10.1109/TSMCB.2009.2035629

L1-norm-based 2DPCA

Xuelong Li^*
, Yanwei Pang
, Yuan Yuan

^*Corresponding author for this work

Computer Science Research Group

Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences
Tianjin University

Research output: Contribution to journal › Article › peer-review

295 Citations (SciVal)

Abstract

In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.

Original language	English
Pages (from-to)	1170-1175
Number of pages	6
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	40
Issue number	4
Early online date	15 Jan 2010
DOIs	https://doi.org/10.1109/TSMCB.2009.2035629
Publication status	Published - Aug 2010

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

L1 norm
outlier
subspace
two-dimensional principal component analysis (2DPCA)

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10.1109/TSMCB.2009.2035629

Cite this

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title = "L1-norm-based 2DPCA",

abstract = "In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.",

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

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AU - Li, Xuelong

AU - Pang, Yanwei

AU - Yuan, Yuan

PY - 2010/8

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AB - In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.

KW - L1 norm

KW - outlier

KW - subspace

KW - two-dimensional principal component analysis (2DPCA)

UR - https://www.scopus.com/pages/publications/77954762644

U2 - 10.1109/TSMCB.2009.2035629

DO - 10.1109/TSMCB.2009.2035629

M3 - Article

C2 - 20083461

AN - SCOPUS:77954762644

SN - 1083-4419

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SP - 1170

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JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IS - 4

ER -

L1-norm-based 2DPCA

Abstract

UN SDGs

Keywords

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