Robust Clustering Based on Finite Mixtures t Distribution
基于有限混合多变量t分布的鲁棒聚类算法

Providing protection against outlier in clustering data is a difficult problem for mixtures models fitting.In this paper,we consider the fitting of mixtures t distributions alternative to mixtures normal distributions for multi-component gauss data with background noise,to improve the robustness of fitting.We propose two modified versions of EM algorithm and integrate them with a model selection criterion respectively,then we get two robust clustering algorithms which can avoid the drawbacks of traditional algorithms(EM/ECM) for solving mixtures t models-highly dependent on initialization and may converge to the boundary of the parameter space,and can also select the number of clusters component automatically by a combined component annihilation strategy.Experiment results show the contrast among different algorithms and demonstrate the effectiveness of our algorithms.