Mixture modelling is a hot area in pattern recognition. This paper focuses on the use of Bernoulli mixtures for binary data and, in particular, for binary images. More specifically, six EM initialisation techniques are described and empirically compared on a classification task of handwritten Indian digits. Somehow surprisingly, we have found that a relatively good initialisation for Bernoulli prototypes is to use slightly perturbed versions of the hypercube centre.


Mixture Models EM Algorithm Multivariate Bernoulli Distribution Initialisation Techniques Binary Data Indian Digits 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Alfons Juan
    • 1
  • José García-Hernández
    • 1
  • Enrique Vidal
    • 1
  1. 1.DSICUniversitat Politècnica de ValènciaValènciaSpain

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