# Cluster weighted models for functional data

arXiv: https://arxiv.org/abs/2503.05159 · HTML: https://mlsanity.com/paper/2503.05159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Modern Non-Linear Function-on-Function Regression](https://mlsanity.com/paper/2107.14151/) (2021)
- [Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model](https://mlsanity.com/paper/1409.6019/) (2014)
- [Outlier detection in multivariate functional data through a contaminated mixture model](https://mlsanity.com/paper/2106.07222/) (2021)
- [Functional regression clustering with multiple functional gene expressions](https://mlsanity.com/paper/2112.00224/) (2021)
- [Unsupervised learning of regression mixture models with unknown number of components](https://mlsanity.com/paper/1409.6981/) (2014)
- [Multivariate response and parsimony for Gaussian cluster-weighted models](https://mlsanity.com/paper/1411.0560/) (2014)
- [Multivariate Cluster Weighted Models Using Skewed Distributions](https://mlsanity.com/paper/2103.09792/) (2021)
- [Matrix Normal Cluster-Weighted Models](https://mlsanity.com/paper/2104.11900/) (2021)

## Built on by (most cited first, 1 of 1)

- [Cluster weighted models with multivariate skewed distributions for functional data](https://mlsanity.com/paper/2504.12683/) (2025)

## Abstract (arXiv)

We propose a method, funWeightClust, based on a family of parsimonious models for clustering heterogeneous functional linear regression data. These models extend cluster weighted models to functional data, and they allow for multivariate functional responses and predictors. The proposed methodology follows the approach used by the the functional high dimensional data clustering (funHDDC) method. We construct an expectation maximization (EM) algorithm for parameter estimation. Using simulated and benchmark data we show that funWeightClust outperforms funHDDC and several two-steps clustering methods. We also use funWeightClust to analyze traffic patterns in Edmonton, Canada.