CutPursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:42474256, 2018.
Abstract
We present an extension of the cutpursuit algorithm, introduced by Landrieu and Obozinski (2017), to the graph totalvariation 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, illconditioned largescale inverse and learning problems, is such that it may in practice extend the scope of application of the totalvariation regularization.
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