Robust t-mixture modelling with SMEM algorithm
Si-Bao Chen
Bin Luo
Key Lab of Intelligent Comput. & Signal Process. of Minist. of Educ., Anhui Univ., Hefei, China;
Abstract
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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