Greedy EM algorithm for robust t-mixture modeling

This paper concerns a greedy EM algorithm for t-mixture modeling, which is more robust than Gaussian mixture modeling when a typical points exist or the set of data has heavy tail. Local Kullback divergence is used to determine how to insert new component. The greedy algorithm obviates the complicated initialization. The results are comparable to that of split-and-merge EM algorithm while the proposed algorithm is faster. Also the by product of a sequence of mixture models is useful for model selection. Experiments of synthetic data clustering and unsupervised color image segmentation are given.

[1]  Geoffrey E. Hinton,et al.  SMEM Algorithm for Mixture Models , 1998, Neural Computation.

[2]  Sibao Chen,et al.  Robust t-mixture modelling with SMEM algorithm , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[3]  Nikos A. Vlassis,et al.  A Greedy EM Algorithm for Gaussian Mixture Learning , 2002, Neural Processing Letters.

[4]  Geoffrey J. McLachlan,et al.  Robust mixture modelling using the t distribution , 2000, Stat. Comput..

[5]  Andrew R. Barron,et al.  Mixture Density Estimation , 1999, NIPS.

[6]  Ben J. A. Kröse,et al.  Efficient Greedy Learning of Gaussian Mixture Models , 2003, Neural Computation.

[7]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .