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Manifold Learning of Vector Fields

  • Hongyu Li
  • I-Fan Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

In this paper, vector field learning is proposed as a new application of manifold learning to vector field. We also provide a learning framework to extract significant features from vector data. Vector data containing position, direction and magnitude information is different from common point data only containing position information. The algorithm of locally linear embedding (LLE) is extended to deal with vector data. The learning ability of the extended version has been tested on synthetic data sets and experimental results demonstrate that the method is very helpful and promising. Manifold features of vector data obtained by learning methods can be used for next work such as classification, clustering, visualization, or segmentation of vectors.

Keywords

Vector Data Locally Linear Embedding Manifold Learn Discrete Vector Magnitude Information 
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

  • Hongyu Li
    • 1
  • I-Fan Shen
    • 1
  1. 1.Department of Computer Science and EngineeringFudan UniversityShanghaiChina

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