Learning Bundle Manifold by Double Neighborhood Graphs

  • Chun-guang Li
  • Jun Guo
  • Hong-gang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)


In this paper, instead of the ordinary manifold assumption, we introduced the bundle manifold assumption that imagines data points lie on a bundle manifold. Under this assumption, we proposed an unsupervised algorithm, named as Bundle Manifold Embedding (BME), to embed the bundle manifold into low dimensional space. In BME, we construct two neighborhood graphs that one is used to model the global metric structure in local neighborhood and the other is used to provide the information of subtle structure, and then apply the spectral graph method to obtain the low-dimensional embedding. Incorporating some prior information, it is possible to find the subtle structures on bundle manifold in an unsupervised manner. Experiments conducted on benchmark datasets demonstrated the feasibility of the proposed BME algorithm, and the difference compared with ISOMAP, LLE and Laplacian Eigenmaps.


Orbit Structure Neighborhood Graph Manifold Learning Nonlinear Dimensionality Reduction Unsupervised Manner 
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 2010

Authors and Affiliations

  • Chun-guang Li
    • 1
  • Jun Guo
    • 1
  • Hong-gang Zhang
    • 1
  1. 1.PRIS lab., School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

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