The particle swarm optimization based parameters determination for Gaussian mixture model

Gaussian mixture model (GMM) is one of the most popular methods to estimate the underlying density function. In this paper, a parameter determination method (PSOGMM) for GMM based on particle swarm optimization (PSO) is proposed. PSOGMM optimizes parameters in GMM based on a new error criterion which is derived based on the integrated square error between the true density function and the estimated density. In order to validate the feasibility and effectiveness of PSOGMM, we carry out some numerical experiments on four types of one-dimensional artificial datasets: Uniform dataset, Normal dataset, Exponential dataset and Rayleigh dataset. The finally comparative results show that our strategies are well-performed and PSOGMM can obtain the better estimation performance when the appropriate parameters are selected for PSO.