Robust mixture modelling using sub-Gaussian stable distribution

2017/01/24 by Teimouri, Mahdi, Rezakhah, Saeid, Mohammdpour, Adel
#FOS: Computer and information sciences #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1701.06749

Abstract

Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian α-stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that estimates parameters of a mixture of sub-Gaussian stable distributions. A comparative study, in the presence of some well-known mixture models, is performed to show the robustness and performance of the mixture of sub-Gaussian α-stable distributions for modelling, simulated, synthetic, and real data.

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