基于多元伪沃伊特分布混合模型的鲁棒基于模型聚类方法
Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions
浏览论文内容
中文总结 AI 辅助
该研究提出多元伪沃伊特分布混合模型，用于鲁棒基于模型聚类与异常值检测，经模拟与真实数据验证，在重尾数据上的聚类及异常值检测性能优于现有模型。
中文摘要 AI 辅助
我们在有限混合建模框架内，提出了伪沃伊特分布（高斯分布与柯西分布的加权凸组合）的多元扩展形式，用于鲁棒基于模型聚类与异常值检测。为确保聚类内部的简洁性与一致性，高斯分量与柯西分量采用共享的位置和尺度参数。参数估计通过期望最大化算法完成，潜在变量可实现高效的似然推断。通过模拟研究及真实数据应用评估该模型的性能，并与现有鲁棒模型（包括污染正态分布混合模型）进行比较，以说明其聚类精度与异常值检测能力。结果表明，该框架对具有重尾特征的数据尤为有效。
英文摘要
We propose a multivariate extension of the pseudo-Voigt profile-a weighted convex combination of Gaussian and Cauchy distributions-within a finite mixture modeling framework for robust model-based clustering and outlier detection. To ensure parsimony and coherence within clusters, shared location and scale parameters are imposed between the Gaussian and Cauchy components. Parameter estimation is carried out via an Expectation Maximization algorithm, with latent variables facilitating efficient likelihood-based inference. The performance of the proposed model is evaluated through simulation studies and applications to real-world data. Comparisons with established robust models, including mixtures of contaminated normal distributions, are provided to illustrate the model's clustering accuracy and outlier detection capabilities. The framework is shown to be particularly effective for data characterized by heavy-tailed behavior.
