Gaussian parsimonious clustering models with covariates and a noise component
Gaussian parsimonious clustering models with covariates and a noise component
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DOI：
10.1007/s11634-019-00373-8
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发表时间：
2017-11
影响因子：
1.6
通讯作者：
Keefe Murphy;T. B. Murphy
中科院分区：
文献类型：
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作者：
Keefe Murphy;T. B. Murphy
通过提出MoEClust模型套件，我们考虑了连续的、相关的数据的基于模型的聚类方法，这些数据考虑了在混合型固定协变量存在下可用的外部信息。这些模型允许协变量的不同子集通过将混合物的参数建模为协变量的函数来影响组件权重和/或组件密度。一个熟悉的范围的约束特征分解参数化的成分协方差矩阵也被容纳。因此，本文解决了将协方差纳入高斯简约聚类模型和将简约协方差结构纳入高斯混合专家框架的所有特殊情况的等效目标。MoEClust模型在单变量和多变量数据集的应用中，从两个角度都证明了显著的改进。还提出了新的扩展，以包括用于捕获异常值的均匀噪声成分，并解决EM算法的初始化，模型选择和结果可视化。
We consider model-based clustering methods for continuous, correlated data that account for external information available in the presence of mixed-type fixed covariates by proposing the MoEClust suite of models. These models allow different subsets of covariates to influence the component weights and/or component densities by modelling the parameters of the mixture as functions of the covariates. A familiar range of constrained eigen-decomposition parameterisations of the component covariance matrices are also accommodated. This paper thus addresses the equivalent aims of including covariates in Gaussian parsimonious clustering models and incorporating parsimonious covariance structures into all special cases of the Gaussian mixture of experts framework. The MoEClust models demonstrate significant improvement from both perspectives in applications to both univariate and multivariate data sets. Novel extensions to include a uniform noise component for capturing outliers and to address initialisation of the EM algorithm, model selection, and the visualisation of results are also proposed.