Pseudo-density Estimation for Clustering with Gaussian Processes

  • Hyun-Chul Kim
  • Jaewook Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


Gaussian processes (GP) provide a kernel machine framework. They have been mainly applied to regression and classification. We propose a pseudo-density estimation method based on the information of variance functions of GPs, which relates to the density of the data points. We also show how the constructed pseudo-density can be applied to clustering. Through simulation we show that the topology of the pseudo-density represents the clustering information well with promising results.


Gaussian Process Sample Function Gaussian Process Regression Gaussian Process Model Kernel Machine 
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

  • Hyun-Chul Kim
    • 1
  • Jaewook Lee
    • 1
  1. 1.Department of Industrial and Management EngineeringPohang University of Science and TechnologyPohang, KyungbukKorea

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