– V. Emiya (LIF) : Compressed sensing with unknown sensor permutation

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Date/heure
Date(s) - 24 janvier 2014

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Compressed sensing with unknown sensor permutation By Valentin Emiya, LIF. Compressed sensing is the ability to retrieve a sparse vector from a set of linear measurements. The task gets more difficult when the sensing process is not perfectly known. We address such a problem in the case where the sensors have been permuted, i.e., the order of the measurements is unknown. We propose a branch-and-bound algorithm that converges to the solution. The experimental study shows that our approach always retrieves the unknown permutation, while a simple convex relaxation strategy almost always fails. In terms of its time complexity, we show that the proposed algorithm converges quickly with respect to the combinatorial nature of the problem.

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Date/heure
Date(s) - 24 janvier 2014

Catégories Pas de Catégories

Compressed sensing with unknown sensor permutation\nBy Valentin Emiya, LIF.\n\nCompressed sensing is the ability to retrieve a sparse vector from a set of linear measurements. The task gets more difficult when the sensing process is not perfectly known. We address such a problem in the case where the sensors have been permuted, i.e., the order of the measurements is unknown. We propose a branch-and-bound algorithm that converges to the solution. The experimental study shows that our approach always retrieves the unknown permutation, while a simple convex relaxation strategy almost always fails. In terms of its time complexity, we show that the proposed algorithm converges quickly with respect to the combinatorial nature of the problem.[

Carte non disponible

Date/heure
Date(s) - 24 janvier 2014

Catégories Pas de Catégories

Compressed sensing with unknown sensor permutation\nBy Valentin Emiya, LIF.\n\nCompressed sensing is the ability to retrieve a sparse vector from a set of linear measurements. The task gets more difficult when the sensing process is not perfectly known. We address such a problem in the case where the sensors have been permuted, i.e., the order of the measurements is unknown. We propose a branch-and-bound algorithm that converges to the solution. The experimental study shows that our approach always retrieves the unknown permutation, while a simple convex relaxation strategy almost always fails. In terms of its time complexity, we show that the proposed algorithm converges quickly with respect to the combinatorial nature of the problem.[

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