Observatorio de I+D+i UPM

Descripción
Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.
Internacional	Si
Nombre congreso	IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Tipo de participación	960
Lugar del congreso	Vancouver (Canadá)
Revisores	Si
ISBN o ISSN	978-1-4799-0356-6
DOI
Fecha inicio congreso	26/05/2013
Fecha fin congreso	31/05/2013
Desde la página	6118
Hasta la página	6122
Título de las actas	Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing

Esta actividad pertenece a memorias de investigación

• Autor: David Luengo Garcia (UPM)

• Creador: Departamento: Ingeniería de Circuitos y Sistemas