Outlier-resisting graph embedding

Yanwei Pang; Yuan Yuan

doi:10.1016/j.neucom.2009.08.020

Outlier-resisting graph embedding

Yanwei Pang, Yuan Yuan

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

53 Scopus citations

Abstract

Graph embedding is a general framework for subspace learning. However, because of the well-known outlier-sensitiveness disadvantage of the L2-norm, conventional graph embedding is not robust to outliers which occur in many practical applications. In this paper, an improved graph embedding algorithm (termed LPP-L1) is proposed by replacing L2-norm with L1-norm. In addition to its robustness property, LPP-L1 avoids small sample size problem. Experimental results on both synthetic and real-world data demonstrate these advantages.

Original language	English
Pages (from-to)	968-974
Number of pages	7
Journal	Neurocomputing
Volume	73
Issue number	4-6
DOIs	https://doi.org/10.1016/j.neucom.2009.08.020
State	Published - Jan 2010
Externally published	Yes

Keywords

Dimensionality reduction
Graph embedding
L1-norm
Locality preserving projection
Outlier

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10.1016/j.neucom.2009.08.020

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abstract = "Graph embedding is a general framework for subspace learning. However, because of the well-known outlier-sensitiveness disadvantage of the L2-norm, conventional graph embedding is not robust to outliers which occur in many practical applications. In this paper, an improved graph embedding algorithm (termed LPP-L1) is proposed by replacing L2-norm with L1-norm. In addition to its robustness property, LPP-L1 avoids small sample size problem. Experimental results on both synthetic and real-world data demonstrate these advantages.",

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author = "Yanwei Pang and Yuan Yuan",

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AB - Graph embedding is a general framework for subspace learning. However, because of the well-known outlier-sensitiveness disadvantage of the L2-norm, conventional graph embedding is not robust to outliers which occur in many practical applications. In this paper, an improved graph embedding algorithm (termed LPP-L1) is proposed by replacing L2-norm with L1-norm. In addition to its robustness property, LPP-L1 avoids small sample size problem. Experimental results on both synthetic and real-world data demonstrate these advantages.

KW - Dimensionality reduction

KW - Graph embedding

KW - L1-norm

KW - Locality preserving projection

KW - Outlier

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JO - Neurocomputing

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ER -

Outlier-resisting graph embedding

Abstract

Keywords

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