Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities
Abstract
Gaussian process regression models can be utilized in recovery of discontinuous signals. Their computational complexity is linear in the number of observations if applied with the covariance functions of nonlinear diffusion. However, such processes often result in hard-to-control jumps of the signal value. Synthetic examples presented in this work indicate that Bayesian evidence-maximizing stopping and knowledge whether signal values are discrete help to outperform the steady state solutions of nonlinear diffusion filtering.
Cite
Text
Girdziusas and Laaksonen. "Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities." European Conference on Machine Learning, 2005. doi:10.1007/11564096_56Markdown
[Girdziusas and Laaksonen. "Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities." European Conference on Machine Learning, 2005.](https://mlanthology.org/ecmlpkdd/2005/girdziusas2005ecml-optimal/) doi:10.1007/11564096_56BibTeX
@inproceedings{girdziusas2005ecml-optimal,
title = {{Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities}},
author = {Girdziusas, Ramunas and Laaksonen, Jorma},
booktitle = {European Conference on Machine Learning},
year = {2005},
pages = {576-583},
doi = {10.1007/11564096_56},
url = {https://mlanthology.org/ecmlpkdd/2005/girdziusas2005ecml-optimal/}
}