The expectation-maximization (EM) algorithm for incomplete data with highly diverse missing data patterns can be computationally expensive. A partial expectation-maximization (PEM) algorithm is developed to ease this computational burden. This PEM algorithm circumvents the need for a traditional E-step by performing a partial E-step that reduces the Kullback-Leibler divergence between the conditional distribution of the missing data and the distribution of the missing data given the observed data. The PEM and EM algorithms are compared in terms of computation time and convergence on simulated data. The PEM algorithm is illustrated using a latent Gaussian mixture model to cluster a white bread sensory analysis dataset.

中文翻译：

---

具有高度多样化缺失数据模式的基于模型的聚类的部分 EM 算法

具有高度多样化缺失数据模式的不完整数据的期望最大化 (EM) 算法的计算成本可能很高。开发了部分期望最大化 (PEM) 算法来减轻这种计算负担。该 PEM 算法通过执行部分 E 步来规避对传统 E 步的需要，该部分 E 步可减少缺失数据的条件分布与给定观测数据的缺失数据分布之间的 Kullback-Leibler 散度。在计算时间和模拟数据的收敛性方面比较了 PEM 和 EM 算法。PEM 算法使用潜在高斯混合模型对白面包感官分析数据集进行聚类。