用偏态分布提升函数型数据聚类效果，更适配非对称数据特征。
Cluster weighted models with multivariate skewed distributions for functional data
- 基于函数线性回归混合模型与三类偏态分布构建聚类方法
- 在模拟数据和空气质量数据上验证了方法的有效性
- 适合处理具有非对称特性的函数型高维数据
我们提出一种名为 funWeightClustSkew 的聚类方法，基于函数型线性回归模型的混合以及三种偏态多元分布：方差-伽马分布、偏斜-t 分布和正态逆高斯分布。该方法遵循函数型高维数据聚类（funHDDC）框架，将适用于有限维多元数据的偏态分布簇加权模型扩展至函数型数据。我们考虑了多种参数简化模型，并构建了期望最大化（EM）算法用于参数估计。通过模拟数据和空气质量数据集展示了 funWeightClustSkew 的性能。
原文摘要 · Abstract (English)
We propose a clustering method, funWeightClustSkew, based on mixtures of functional linear regression models and three skewed multivariate distributions: the variance-gamma distribution, the skew-t distribution, and the normal-inverse Gaussian distribution. Our approach follows the framework of the functional high dimensional data clustering (funHDDC) method, and we extend to functional data the cluster weighted models based on skewed distributions used for finite dimensional multivariate data. We consider several parsimonious models, and to estimate the parameters we construct an expectation maximization (EM) algorithm. We illustrate the performance of funWeightClustSkew for simulated data and for the Air Quality dataset.
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