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.
Hugo Raguet (LIVE (CNRS))
loic landrieu (IGN)
Reasearcher in MATIS team at IGN. Focus on structured learning and optimization with application to remote sensing.
Related Events (a corresponding poster, oral, or spotlight)
2018 Oral: Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation »
Fri Jul 13th 03:30 -- 03:40 PM Room A9