Robust Estimation of Unbalanced Mixture Models on Samples with Outliers

  title={Robust Estimation of Unbalanced Mixture Models on Samples with Outliers},
  author={A. Galimzianova and F. Pernus and B. Likar and Ziga Spiclin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  • A. Galimzianova, F. Pernus, +1 author Ziga Spiclin
  • Published 2015
  • Medicine, Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Mixture models are often used to compactly represent samples from heterogeneous sources. However, in real world, the samples generally contain an unknown fraction of outliers and the sources generate different or unbalanced numbers of observations. Such unbalanced and contaminated samples may, for instance, be obtained by high density data sensors such as imaging devices. Estimation of unbalanced mixture models from samples with outliers requires robust estimation methods. In this paper, we… CONTINUE READING
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    Publications referenced by this paper.
    Robust fitting of mixtures using the trimmed likelihood estimator
    • 149
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    Robust mixture regression using the t-distribution
    • 50
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    Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions
    • 63
    Robust estimation in the normal mixture model based on robust clustering
    • 37
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    Model-based clustering and classification with non-normal mixture distributions
    • 66
    Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients
    • 46
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    A robust method for cluster analysis
    • 94
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