一种快速有效的高斯混合信号模态期望最大化算法
A fast and efficient Modal EM algorithm for Gaussian mixtures
摘要：
在模态聚类方法中，聚类被定义为潜在概率密度函数的局部最大值，其中后者可以通过非参数化或使用有限混合模型来估计。因此，簇与密度模式周围的某些区域密切相关，并且每个簇对应于密度的凸起。模态期望最大化算法是一种迭代过程，可以识别任何密度函数的局部最大值。在这方面的贡献，我们提出了一个快速，高效的模态期望最大化算法时使用的密度函数估计通过有限的混合高斯分布与简约的成分协方差结构。在描述了过程之后，我们将所提出的模态期望最大化算法应用于模拟和真实数据示例，在多种情况下显示出其高度的灵活性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
