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Robust ISOMAP Based on Neighbor Ranking Metric

  • Chun Du
  • Shilin Zhou
  • Jixiang Sun
  • Jingjing Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

ISOMAP is one of classical manifold learning methods that can discover the low-dimensional nonlinear structure automatically in a high-dimensional data space. However, it is very sensitive to the outlier, which is a great disadvantage to its applications. To solve the noisy manifold learning problem, this paper proposes a robust ISOMAP based on neighbor ranking metric (NRM). Firstly, NRM is applied to remove outliers partially, then a two-step strategy is adopted to select suitable neighbors for each point to construct neighborhood graph. The experimental results indicate that the method can effectively improve robustness in noisy manifold learning both on synthetic and real-world data.

Keywords

ISOMAP noisy manifold learning neighbor ranking metric 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chun Du
    • 1
  • Shilin Zhou
    • 1
  • Jixiang Sun
    • 1
  • Jingjing Zhao
    • 1
  1. 1.School of Electronic Science and EngineeringNational University of Defense TechnologyChangshaP.R. China

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