Neighbor Line-Based Locally Linear Embedding

  • De-Chuan Zhan
  • Zhi-Hua Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


Locally linear embedding (Lle) is a powerful approach for mapping high-dimensional data nonlinearly to a lower-dimensional space. However, when the training examples are not densely sampled, Lle often returns invalid results. In this paper, the Nl 3 e (Neighbor Line-based Lle) approach is proposed, which generates some virtual examples with the help of neighbor line such that the Lle learning can be executed on an enriched training set. Experiments show that Nl 3 e outperforms Lle in visualization.


Face Recognition Query Point Minority Class Dimensionality Reduction Method Linear Embedding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • De-Chuan Zhan
    • 1
  • Zhi-Hua Zhou
    • 1
  1. 1.National Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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