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Cross-Products LASSO
Cita
Luengo García, David 
ORCID: https://orcid.org/0000-0001-7407-3630, Vía Rodríguez, Javier, Monzón García, Sandra, Trigano, Tom and Artés Rodríguez, Antonio
(2013).
Cross-Products LASSO.
En: "38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)", 26/05/2013 - 31/05/2013, Vancouver (Canadá). ISBN 978-1-4799-0356-6. pp. 6118-6122.
ORCID: https://orcid.org/0000-0001-7407-3630, Vía Rodríguez, Javier, Monzón García, Sandra, Trigano, Tom and Artés Rodríguez, Antonio
(2013).
Cross-Products LASSO.
En: "38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)", 26/05/2013 - 31/05/2013, Vancouver (Canadá). ISBN 978-1-4799-0356-6. pp. 6118-6122.
Descripción
| Título: | Cross-Products LASSO |
|---|---|
| Autor/es: |
|
| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
| Fechas del Evento: | 26/05/2013 - 31/05/2013 |
| Lugar del Evento: | Vancouver (Canadá) |
| Título del Libro: | Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) |
| Fecha: | 2013 |
| ISBN: | 978-1-4799-0356-6 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | negative co-occurrence, sparsity-aware learning, LASSO, sparse coding |
| Escuela: | E.U.I.T. Telecomunicación (UPM) [antigua denominación] |
| Departamento: | Ingeniería de Circuitos y Sistemas [hasta 2014] |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
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.
Más información
| ID de Registro: | 33284 |
|---|---|
| Identificador DC: | https://oa.upm.es/33284/ |
| Identificador OAI: | oai:oa.upm.es:33284 |
| URL Oficial: | https://www2.securecms.com/ICASSP2013/default.asp |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 15 Abr 2015 15:34 |
| Ultima Modificación: | 15 Abr 2015 17:15 |
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