Discriminant adaptive edge weights for graph embedding

Yuan Yuan*, Yanwei Pang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

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 publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages1993-1996
Number of pages4
DOIs
Publication statusPublished - 16 Sep 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United Kingdom
Duration: 31 Mar 20084 Apr 2008

Conference

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

Keywords

  • Edge weights
  • Graph embedding

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