Clustering with a Semantic Criterion Based on Dimensionality Analysis

  • Wenye Li
  • Kin-Hong Lee
  • Kwong-Sak Leung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


Considering data processing problems from a geometric point of view, previous work has shown that the intrinsic dimension of the data could have some semantics. In this paper, we start from the consideration of this inherent topology property and propose the usage of such a semantic criterion for clustering. The corresponding learning algorithms are provided. Theoretical justification and analysis of the algorithms are shown. Promising results are reported by the experiments that generally fail with conventional clustering algorithms.


Intrinsic Dimension Face Image Dimensionality Analysis Expectation Maximization Algorithm Neural Information Processing System 
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

  • Wenye Li
    • 1
  • Kin-Hong Lee
    • 1
  • Kwong-Sak Leung
    • 1
  1. 1.The Chinese University of Hong KongShatin N.T., Hong KongChina P.R.

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