THE PARTICLE SWARM OPTIMIZATION BASED PARAMETERS DETERMINATION FOR GAUSSIAN MIXTURE MODEL

摘要：

　　Ganssian 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.

关键词： Gaussian mixture model Density estimation Particle swarm optimization Integrated square error GMM PSO PSOGMM

作者: HUI-BIN WANG YUN HOU XIN WANG

作者单位: Academic Affairs Office, Xingtai University, Xingtai 054001, China;Department of Information Science Department of Information Science and Technology, Xingtai University, Xingtai 054001, China Department of Computer Science and Technology, Tangshan College, Tangshan 063000, China

会议类型: 国际会议

会议名称: 2011 International Conference on Wavelet Analysis and Pattern Recognition(2011小波分析与模式识别国际会议)

会议地点: 桂林

会议语种:英文

页码: 150-155

在线出版日期: 2011-07-10（万方平台首次上网日期，不代表论文的发表时间）