摘要
基于最优估计函数,给出了一种估计得分函数的方法.通过使用高斯混合模型,给出了估计信号概率密度的EM算法和进行独立分量分析优化的梯度算法.为了提高算法的精度和稳定度,发展了迭代估计概率密度的方法,该方法可以针对超、亚混合信号进行分离.
Independent component analysis （ICA） is a method for finding independent components from multivariate （multidimensional） statistical data. Based on the optimal estimation function, a method for the estimation of the score function is developed. By using the Gaussian mixture model , an EM algorithm for approximating the probability density of the data is presented, and a stochastic gradient method is given to separate the independent components. To improve the accuracy and stability of the algorithm, an iterative method for estimating the PDF of data is presented, which can perform the separation of mixed sub-Gaussian from super-Gaussian sources. The performance of the method is shown by computer simulations.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2005年第4期574-578,共5页
Journal of Xidian University
基金
国家自然科学基金资助项目(60072043)
关键词
独立分量分析
梯度下降法
高斯混合模型
串音误差
independent component analysis
gradient steepest ascent
Gaussian mixture modeling
crosstalk error
