Evolving Insight into High-Dimensional Data

  • Yiqing Tu
  • Gang Li
  • Honghua Dai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3644)


ISOMap is a popular method for nonlinear dimensionality reduction in batch mode, but need to run its entirety inefficiently if the data comes sequentially. In this paper, we present an extension of ISOMap, namely I-ISOMap, augmenting the existing ISOMap framework to the situation where additional points become available after initial manifold is constructed. The MDS step, as a key component in ISOMap, is adapted by introducing Spring model and sampling strategy. As a result, it consumes only linear time to obtain a stable layout due to the Spring model’s iterative nature. The proposed method outperforms earlier work by Law [1], where their MDS step runs within quadratic time. Experimental results show that I-ISOMap is a precise and efficient technique for capturing evolving manifold.


Feature Space Residual Variance Geodesic Distance Point Pair Neighborhood Graph 
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 2005

Authors and Affiliations

  • Yiqing Tu
    • 1
  • Gang Li
    • 1
  • Honghua Dai
    • 1
  1. 1.School of Information TechnologyDeakin UniversityAustralia

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