基于协方差的高斯混合模型参数学习算法

Covariance Based Learning Algorithm for Gaussian Mixture Model

对混合高斯模型参数估计问题的算法通常是基于期望最大(Expectation Maximization)给出的。在混合高斯模型的因素协方差矩阵已知、因素各分量独立的前提下,给出了基于协方差矩阵的机器学习算法,简称CVB(Covariance Based)算法,并进行了一定的数学分析。最后给出了与期望最大算法的实验结果比较。实验结果表明,在该条件下,基于协方差的算法优于期望最大算法。

Expectation maximization is commonly used for parameter estimation in Gaussian mixture model. This paper presented a machine learning algorithm based on covariance（CVB） for solving the Gaussian mixture model with the specific constrain that eovariance is already known. Experiments show that the CVB algorithm has better performance than the EM algorithm with regard to the specific constraint.