论文标题
高斯混合物的快速有效的模态EM算法
A fast and efficient Modal EM algorithm for Gaussian mixtures
论文作者
论文摘要
在聚类的模态方法中，簇被定义为潜在概率密度函数的局部最大值，其中可以非参数估算后者或使用有限的混合模型。因此，簇与密度模式周围的某些区域密切相关，每个簇都对应于密度的凸起。模态EM算法是一个迭代过程，可以识别任何密度函数的局部最大值。在这一贡献中，我们提出了一种快速有效的模态EM算法，当通过高斯分布的有限混合物及其质量分量协方差结构估算密度函数时。在描述了该过程之后，我们将所提出的模态EM算法应用于模拟和真实数据示例，显示其在多种情况下的灵活性很高。
In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either non-parametrically or using finite mixture models. Thus, clusters are closely related to certain regions around the density modes, and every cluster corresponds to a bump of the density. The Modal EM algorithm is an iterative procedure that can identify the local maxima of any density function. In this contribution, we propose a fast and efficient Modal EM algorithm to be used when the density function is estimated through a finite mixture of Gaussian distributions with parsimonious component-covariance structures. After describing the procedure, we apply the proposed Modal EM algorithm on both simulated and real data examples, showing its high flexibility in several contexts.
