ICML 2018oral11 citations
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.
BibTeX
@InProceedings{pmlr-v80-raguet18a,
title = {Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation},
author = {Raguet, Hugo and Landrieu, Loic},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {4247--4256},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/raguet18a/raguet18a.pdf},
url = {https://proceedings.mlr.press/v80/raguet18a.html},
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.}
}