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Discriminant Graph Based Linear Embedding

  • Bo Li
  • Jin Liu
  • Wen-Yong Dong
  • Wen-Sheng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

LLE is a nonlinear dimensionality reduction method, which has been successfully applied to data visualization. Based on the assumption of local linearity, LLE can compute the weights between the KNN nodes using the local least reconstruction errors, which increase the computational cost. In this paper, a method titled Discriminant Graph Based Linear Embedding (DGBLE) is proposed to set the weights between the nodes in the KNN graph directly to reduce the computational expense. Moreover, label information can also be taken into account to improve the discriminant power of the original LLE. Experiments on some benchmark data show that the proposed method is feasible and effective.

Keywords

LLE graph linear embedding 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bo Li
    • 1
    • 2
    • 4
  • Jin Liu
    • 3
    • 4
  • Wen-Yong Dong
    • 3
    • 4
  • Wen-Sheng Zhang
    • 4
  1. 1.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.State Key Lab. for Novel Software TechnologyNanjingChina
  3. 3.State Key Lab. of Software EngineeringWuhanChina
  4. 4.Institute of AutomationChinese Academy of ScienceBeijingChina

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