# Mixture of segmentation for heterogeneous functional data

arXiv: https://arxiv.org/abs/2303.10712 · HTML: https://mlsanity.com/paper/2303.10712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [On Optimal Multiple Changepoint Algorithms for Large Data](https://mlsanity.com/paper/1409.1842/) (2014)
- [Model-based clustering and segmentation of time series with changes in regime](https://mlsanity.com/paper/1312.6967/) (2013)
- [Piecewise regression mixture for simultaneous functional data clustering and optimal segmentation](https://mlsanity.com/paper/1312.6974/) (2013)
- [Co-clustering of time-dependent data via Shape Invariant Model](https://mlsanity.com/paper/2104.03083/) (2021)

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

- [Enriched Functional Tree-Based Classifiers: A Novel Approach Leveraging Derivatives and Geometric Features](https://mlsanity.com/paper/2409.17804/) (2024)

## Abstract (arXiv)

In this paper we consider functional data with heterogeneity in time and in population. We propose a mixture model with segmentation of time to represent this heterogeneity while keeping the functional structure. Maximum likelihood estimator is considered, proved to be identifiable and consistent. In practice, an EM algorithm is used, combined with dynamic programming for the maximization step, to approximate the maximum likelihood estimator. The method is illustrated on a simulated dataset, and used on a real dataset of electricity consumption.