Discrete Mumford-Shah on graph for mixing matrix estimation Article - Septembre 2019

Yacouba Kaloga, Marion Foare, Nelly Pustelnik, Pablo Jensen

Yacouba Kaloga, Marion Foare, Nelly Pustelnik, Pablo Jensen, « Discrete Mumford-Shah on graph for mixing matrix estimation  », IEEE Signal Processing Letters, septembre 2019, pp. 1275-1279. ISSN 1070-9908

Abstract

The discrete Mumford-Shah formalism has been introduced for the image denoising problem, allowing to capture both smooth behavior inside an object and sharp transitions on the boundary. In the present work, we propose first to extend this formalism to graphs and to the problem of mixing matrix estimation. New algorithmic schemes with convergence guarantees relying on proximal alternating minimization strategies are derived and their efficiency (good estimation and robustness to initialization) are evaluated on simulated data, in the context of vote transfer matrix estimation.

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