# A hidden process regression model for functional data description. Application to curve discrimination

arXiv: https://arxiv.org/abs/1312.6968 · HTML: https://mlsanity.com/paper/1312.6968/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2013.

## Built on by (most cited first, 3 of 3)

- [Demystifying Softmax Gating Function in Gaussian Mixture of Experts](https://mlsanity.com/paper/2305.03288/) (2023)
- [A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models](https://mlsanity.com/paper/2104.02640/) (2021)
- [An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling](https://mlsanity.com/paper/1707.03538/) (2017)

## Abstract (arXiv)

A new approach for functional data description is proposed in this paper. It consists of a regression model with a discrete hidden logistic process which is adapted for modeling curves with abrupt or smooth regime changes. The model parameters are estimated in a maximum likelihood framework through a dedicated Expectation Maximization (EM) algorithm. From the proposed generative model, a curve discrimination rule is derived using the Maximum A Posteriori rule. The proposed model is evaluated using simulated curves and real world curves acquired during railway switch operations, by performing comparisons with the piecewise regression approach in terms of curve modeling and classification.