Efficient First-Order Algorithms for Adaptive Signal Denoising
Abstract
We consider the problem of discrete-time signal denoising, focusing on a specific family of non-linear convolution-type estimators. Each such estimator is associated with a time-invariant filter which is obtained adaptively, by solving a certain convex optimization problem. Adaptive convolution-type estimators were demonstrated to have favorable statistical properties, see (Juditsky & Nemirovski, 2009; 2010; Harchaoui et al., 2015b; Ostrovsky et al., 2016). Our first contribution is an efficient implementation of these estimators via the known first-order proximal algorithms. Our second contribution is a computational complexity analysis of the proposed procedures, which takes into account their statistical nature and the related notion of statistical accuracy. The proposed procedures and their analysis are illustrated on a simulated data benchmark.
Cite
Text
Ostrovskii and Harchaoui. "Efficient First-Order Algorithms for Adaptive Signal Denoising." International Conference on Machine Learning, 2018.Markdown
[Ostrovskii and Harchaoui. "Efficient First-Order Algorithms for Adaptive Signal Denoising." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/ostrovskii2018icml-efficient/)BibTeX
@inproceedings{ostrovskii2018icml-efficient,
title = {{Efficient First-Order Algorithms for Adaptive Signal Denoising}},
author = {Ostrovskii, Dmitrii and Harchaoui, Zaid},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {3946-3955},
volume = {80},
url = {https://mlanthology.org/icml/2018/ostrovskii2018icml-efficient/}
}