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Communication dans un congrès

A subspace approach for shrinkage parameter selection in undersampled configuration for Regularised Tyler Estimators

Abstract : Regularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered application. Thus, finding a generic rule that is optimal for every criterion and/or data configurations is not straightforward. This issue is addressed in this paper for undersampled configurations (number of samples lower than the dimension of the data). The paper proposes a new regularisation parameter selection based on a subspace reduction approach. The performance of this method is investigated in terms of estimation accuracy and for adaptive detection purposes, both on simulation and real data.
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Communication dans un congrès
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https://hal.univ-grenoble-alpes.fr/hal-01617054
Contributeur : Guillaume Ginolhac <>
Soumis le : lundi 16 octobre 2017 - 09:29:52
Dernière modification le : mercredi 14 octobre 2020 - 04:14:20
Archivage à long terme le : : mercredi 17 janvier 2018 - 12:22:00

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Quentin Hoarau, Arnaud Breloy, Guillaume Ginolhac, Abdourrahmane Atto, Jean Marie Nicolas. A subspace approach for shrinkage parameter selection in undersampled configuration for Regularised Tyler Estimators. ICASSP 2017, Sep 2017, New Orleans, United States. pp.3291 - 3295, ⟨10.1109/ICASSP.2017.7952765⟩. ⟨hal-01617054⟩

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