Scholarlink

We propose a method for clustering multivariate functional linear regression data. Our approach extends multivariate cluster weighted models [3] to functional data with multivariate functional response and predictors, based on the ideas used by the funHDDC method [5]. To add model flexibility, we consider several two-component parsimonious models by combining the parsimonious models used for funHDDC with the Gaussian parsimonious clustering models family in [1]. Parameter estimation is carried out within the expectation maximization (EM) algorithm framework. The proposed method outperforms funHDDC on simulated and real-world data.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Multivariate Functional Data Clustering Method Using Parsimonious Cluster Weighted Models

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Cristina Adela Anton,
  • Iain Smith

摘要

We propose a method for clustering multivariate functional linear regression data. Our approach extends multivariate cluster weighted models [3] to functional data with multivariate functional response and predictors, based on the ideas used by the funHDDC method [5]. To add model flexibility, we consider several two-component parsimonious models by combining the parsimonious models used for funHDDC with the Gaussian parsimonious clustering models family in [1]. Parameter estimation is carried out within the expectation maximization (EM) algorithm framework. The proposed method outperforms funHDDC on simulated and real-world data.