Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Abstract
We present an extension of the cut-pursuit algorithm, introduced by Landrieu and Obozinski (2017), to the graph total-variation regularization of functions with a separable nondifferentiable part. We propose a modified algorithmic scheme as well as adapted proofs of convergence. We also present a heuristic approach for handling the cases in which the values associated to each vertex of the graph are multidimensional. The performance of our algorithm, which we demonstrate on difficult, ill-conditioned large-scale inverse and learning problems, is such that it may in practice extend the scope of application of the total-variation regularization.
Cite
Text
Raguet and Landrieu. "Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation." International Conference on Machine Learning, 2018.Markdown
[Raguet and Landrieu. "Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/raguet2018icml-cutpursuit/)BibTeX
@inproceedings{raguet2018icml-cutpursuit,
title = {{Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation}},
author = {Raguet, Hugo and Landrieu, Loic},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {4247-4256},
volume = {80},
url = {https://mlanthology.org/icml/2018/raguet2018icml-cutpursuit/}
}