EM Algorithm for Partially Known Labels

  • C. Ambroise
  • G. Govaert
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Mixture models are widely used for clustering or discrimination problems. Estimating the parameters of such models can be viewed as an incomplete data problem and has thus often been handled by the Expectation-Maximization (EM) algorithm. It has been shown that this method can integrate additional information such as the label of some observations. In this paper we propose a generalization of this approach which can take into account partial information about the observation labels. An example illustrates the relevance of the proposed method for mixture density estimation.


Mixture Model Additional Knowledge Label Indicator Finite Mixture Distribution Component Gaussian Mixture 
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 2000

Authors and Affiliations

  • C. Ambroise
    • 1
  • G. Govaert
    • 1
  1. 1.UMR CNRS 6599, Centre de recherches de RoyallieuCompiègne cedexFrance

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