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1 IMAGINE Marne-la-Vallée 2 LIGM - Laboratoire d-Informatique Gaspard-Monge 3 CREST - Centre de Recherche en Économie et Statistique

Abstract : In this paper, we develop a novel approach to the problem of learning sparse representations in the context of fused sparsity and unknown noise level. We propose an algorithm, termed Scaled Fused Dantzig Selector SFDS, that accomplishes the aforementioned learning task by means of a second-order cone program. A special emphasize is put on the particular instance of fused sparsity corresponding to the learning in presence of outliers. We establish finite sample risk bounds and carry out an experimental evaluation on both synthetic and real data.

Keywords : sparse regression robust estimation indirect sparsity sparse linear model variance estimation





Autor: Yin Chen - Arnak S. Dalalyan -

Fuente: https://hal.archives-ouvertes.fr/



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