基于最大惩罚似然的高斯混合模型无监督分类研究

In this paper we propose an unsupervised classification algorithm which is based on Gaussian mixture models. Thinking that EM algorithm will result in a local optimal resolution of Gaussian mixture models in parameter estimations, we substitute invert Wishart distribution for Jeffery prior. Experiments show that this algorithm improves correct rates and decreases time while estimating classifications.