An attraction-repulsion expectation-maximization (AREM) algorithm for density estimation is proposed in this paper. We introduce a Gibbs distribution function for attraction and inverse Gibbs distribution for repulsion as an augmented penalty function in order to determine equilibrium between over-smoothing and over-fitting. The logarithm of the likelihood function augmented the Gibbs density mixture is solved under expectation-maximization (EM) method. We demonstrate the application of the proposed attraction-repulsion expectation-maximization algorithm to image reconstruction and sensor field estimation problem using computer simulation. We show that the proposed algorithm improves the performance considerably.
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