Robust Bayesian clustering

@article{Archambeau2007RobustBC,
  title={Robust Bayesian clustering},
  author={C{\'e}dric Archambeau and Michel Verleysen},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2007},
  volume={20 1},
  pages={
          129-38
        }
}
A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer approach. They are commonly used for density estimation and clustering tasks, but are sensitive to outliers. The Student-t distribution has heavier tails than the Gaussian distribution and is therefore… CONTINUE READING

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