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Article Dans Une Revue IEEE Transactions on Signal Processing Année : 2016

On-The-Fly Approximation of Multivariate Total Variation Minimization

Résumé

In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for univariate data. In this contribution, an extension of such an on-the-fly strategy to multivariate data is investigated. The proposed algorithm relies on the local validation of the Karush-Kuhn-Tucker conditions on the dual problem. Showing that the non-local nature of the multivariate setting precludes to obtain an exact on-the-fly solution, we devise an on-the-fly algorithm delivering an approximate solution, whose quality is controlled by a practitioner-tunable parameter, acting as a trade-off between quality and computational cost. Performance assessment shows that high quality solutions are obtained on-the-fly while benefiting of computational costs several orders of magnitude lower than standard iterative procedures. The proposed algorithm thus provides practitioners with an efficient multivariate change-point detection on-the-fly procedure.
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Dates et versions

hal-01398864 , version 1 (17-11-2016)

Identifiants

Citer

Jordan Frecon, Nelly Pustelnik, Patrice Abry, Laurent Condat. On-The-Fly Approximation of Multivariate Total Variation Minimization. IEEE Transactions on Signal Processing, 2016, 64 (9), pp.2355 - 2364. ⟨10.1109/TSP.2016.2516962⟩. ⟨hal-01398864⟩
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