Outlier-resisting graph embedding

Yanwei Pang; Yuan Yuan

doi:10.1016/j.neucom.2009.08.020

Outlier-resisting graph embedding

Yanwei Pang
, Yuan Yuan^*

^*Corresponding author for this work

Computer Science Research Group

Tianjin University

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

55 Citations (Scopus)

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
Early online date	9 Oct 2009
DOIs	https://doi.org/10.1016/j.neucom.2009.08.020
Publication status	Published - Jan 2010

Bibliographical note

Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)

Keywords

dimensionality reduction
graph embedding
L1-norm
locality preserving projection
outlier

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10.1016/j.neucom.2009.08.020

Cite this

@article{3572ae2bff764207a988a4048fb6b626,

title = "Outlier-resisting graph embedding",

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.",

keywords = "dimensionality reduction, graph embedding, L1-norm, locality preserving projection, outlier",

author = "Yanwei Pang and Yuan Yuan",

note = "Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)",

year = "2010",

month = jan,

doi = "10.1016/j.neucom.2009.08.020",

language = "English",

volume = "73",

pages = "968--974",

journal = "Neurocomputing",

issn = "0925-2312",

publisher = "Elsevier",

number = "4-6",

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TY - JOUR

T1 - Outlier-resisting graph embedding

AU - Pang, Yanwei

AU - Yuan, Yuan

N1 - Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)

PY - 2010/1

Y1 - 2010/1

N2 - 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.

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

UR - https://www.scopus.com/pages/publications/75749094310

U2 - 10.1016/j.neucom.2009.08.020

DO - 10.1016/j.neucom.2009.08.020

M3 - Article

AN - SCOPUS:75749094310

SN - 0925-2312

VL - 73

SP - 968

EP - 974

JO - Neurocomputing

JF - Neurocomputing

IS - 4-6

ER -

Outlier-resisting graph embedding

Abstract

Bibliographical note

Keywords

Access to Document

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