Robust t-mixture modelling with SMEM algorithm
Si-Bao Chen; Bin Luo
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Volume 6, Issue , 26-29 Aug. 2004 Page(s): 3689 - 3694 vol.6
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Summary: Multivariate t-mixture modelling is more robust than Gaussian mixture modelling to a set of data containing a group or groups of observations with longer than Gaussian tails or a typical observations. To alleviate the problem of local convergence of the traditional EM algorithm, a split-and-merge operation is introduced into the EM algorithm for multivariate t-mixtures. The split-and-merge equations are first presented theoretically and then a new merge method is acquired. Accordingly, a modified EM algorithm is constructed. Experiments of data clustering and unsupervised color image segmentation are given.

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