基于最大惩罚似然的高斯混合模型无监督分类研究
基于最大惩罚似然的高斯混合模型无监督分类研究
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摘要: 本文提出了一个基于高斯混合模型的无监督分类算法. 考虑到利用EM算法求解高斯混合模型的参数参数估计问题容易陷入局部最优解, 我们引入逆Wishart分布来代替传统的Jeffery先验. 几个实验数据的结果表明, 采用该方法估计无监督分类的成分数, 无论是估计的正确率, 还是运算速度, 都有较大提高.Abstract: 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.