Título:
Multimodal graphical models via Group Lasso
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Autor:
Hariri, Ahamd
Musé, Pablo
Fiori, Marcelo
Sapiro, Guillermo
Musé, Pablo
Fiori, Marcelo
Sapiro, Guillermo
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Descriptores:
Procesamiento de Señales
Año de publicación:
2013
Contenido:
Resumen:
Graphical models are a very useful tool to describe and understand natural phenomena, from gene expression and brain networks to climate change and social interactions. In many cases, the data is multimodal. For example, one may want to build one network from several fMRI (functional magnetic resonance imaging) studies from different subjects, or combine different data modalities (as fMRI and questions) for several subjects. To this end, in this work we combine group lasso with graphical lasso, and derive an iterative shrinkage thresholding algorithm for solving the proposed optimization problem. The framework is validated with synthetic data and real fMRI data, showing the advantages of combining different modalities in order to infer the underlying network structure.
Descripción:
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EN:
Signal Processing with Adaptive Sparse Structured Representations. SPARS 2013. EPFL, Lausanne, 8-11 jul., 2013.
Financiadores:
Citación:
Hariri, A, Musé, P, Fiori, M, Sapiro, G. "Multimodal graphical models via Group Lasso". Signal Processing with Adaptive Sparse Structured Representations. SPARS 2013. EPFL, Lausanne, 8-11 jul., 2013.
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imágenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| Mutimoldal graphical.pdf | — | 140.88 KB | Adobe PDF |