Abstract

When a data set resides on a d-dimensional manifold that is not a subspace, linear methods cannot effectively reduce the data dimension. Nonlinear methods need to be applied in this case. In this chapter, we introduce Isomap method, which unfolds a manifold by keeping the geodesic metric on the original data set. The description of the method is given in Section 8.1, and the Isomap algorithm is presented in Section 8.2. In Section 8.3, we introduce Dijkstra’s algorithm that effectively computes graph distances. The experiments and applications of Isomaps are included in Section 8.4. We present the justification of Isomaps in Section 8.5.

Keywords

Dimensionality Reduction Geodesic Distance Neighborhood Size Graph Distance Path Distance 
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

© Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jianzhong Wang
    • 1
  1. 1.Department of Mathematics and StatisticsSam Houston State UniversityHuntsvilleUSA

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