Functional Mixture Discriminant Analysis with hidden process regression for curve classification
Methodology
2013-12-30 v1 Machine Learning
Machine Learning
Abstract
We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible curve-models to each class of complex-shaped curves presenting regime changes. The model parameters are learned by maximizing the observed-data log-likelihood for each class by using a dedicated expectation-maximization (EM) algorithm. Comparisons on simulated data with alternative approaches show that the proposed approach provides better results.
Cite
@article{arxiv.1312.7007,
title = {Functional Mixture Discriminant Analysis with hidden process regression for curve classification},
author = {Faicel Chamroukhi and Heré Glotin and Céline Rabouy},
journal= {arXiv preprint arXiv:1312.7007},
year = {2013}
}
Comments
In Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Pages 281-286, 2012, Bruges, Belgium