This paper studied PCA mixture model in high dimensional space. A novel EM learning approach by using perturbation was proposed for the PCA mixture model. Experiments showed the novel perturbation EM algorithm is more effective in learning PCA mixture model than an existing constrained EM algorithm.


Mixture Model Gaussian Mixture Model Principal Direction High Dimensional Space Neural Computation 
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 2004

Authors and Affiliations

  • Zhong Jin
    • 1
    • 3
  • Franck Davoine
    • 2
  • Zhen Lou
    • 3
  1. 1.Centre de Visio per ComputadorUniversitat Autonoma de BarcelonaBarcelonaSpain
  2. 2.Heudiasyc – CNRS Mixed Research UnitCompiegne University of TechnologyCompiegne cedexFrance
  3. 3.Department of Computer ScienceNanjing University of Science and TechnologyNanjingPeople’s Republic of China

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