Discriminant adaptive edge weights for graph embedding

Yuan Yuan, Yanwei Pang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Many linear dimensionality reduction (LDR) methods, such as PCA and LDA, can be reformulated in the framework of graph embedding (GE). In this framework, those LDR methods are differentiated by values of edge weights of a graph. This paper first proposes a linear dimensionality reduction method, which assigns edges with discriminant adaptive weights. Specifically, we compute a local decision hyper-plane by using support vector machine (SVM). Then edge weighs corresponding to the local region are expressed as a function of the angle between the direction of the edges and the normal vector of the hyper-plane. Experimental results demonstrate the advantages of this proposed method.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages1993-1996
Number of pages4
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: 31 Mar 20084 Apr 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryUnited States
CityLas Vegas, NV
Period31/03/084/04/08

Keywords

  • Edge weights
  • Graph embedding

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