Outlier-resisting graph embedding

Yanwei Pang, Yuan Yuan*

*Corresponding author for this work

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)968-974
Number of pages7
JournalNeurocomputing
Volume73
Issue number4-6
Early online date9 Oct 2009
DOIs
Publication statusPublished - 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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