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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 this letter, 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) is evaluated on simulated data, in the context of vote transfer matrix estimation.
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