Multimodal graphical models via Group Lasso
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.
| 2013 | |
| Procesamiento de Señales | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/41761 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522992971841536 |
|---|---|
| author | Hariri, Ahamd |
| author2 | Musé, Pablo Fiori, Marcelo Sapiro, Guillermo |
| author2_role | author author author |
| author_facet | Hariri, Ahamd Musé, Pablo Fiori, Marcelo Sapiro, Guillermo |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Hariri, Ahamd Musé, Pablo Fiori, Marcelo Sapiro, Guillermo |
| dc.date.accessioned.none.fl_str_mv | 2023-12-11T19:57:39Z |
| dc.date.available.none.fl_str_mv | 2023-12-11T19:57:39Z |
| dc.date.issued.es.fl_str_mv | 2013 |
| dc.date.submitted.es.fl_str_mv | 20231211 |
| dc.description.abstract.none.fl_txt_mv | 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. |
| dc.identifier.citation.es.fl_str_mv | 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. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41761 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.relation.ispartof.es.fl_str_mv | Signal Processing with Adaptive Sparse Structured Representations. SPARS 2013. EPFL, Lausanne, 8-11 jul., 2013. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Multimodal graphical models via Group Lasso |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_0eb0114b965f49497fc7b6b2958cb023 |
| identifier_str_mv | 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. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41761 |
| publishDate | 2013 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-12-11T19:57:39Z2023-12-11T19:57:39Z201320231211Hariri, 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.https://hdl.handle.net/20.500.12008/41761Graphical 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.Made available in DSpace on 2023-12-11T19:57:39Z (GMT). No. of bitstreams: 5 Mutimoldal graphical.pdf: 144263 bytes, checksum: cbbd00b944931b8f39c83276bc04385c (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2013enengSignal Processing with Adaptive Sparse Structured Representations. SPARS 2013. EPFL, Lausanne, 8-11 jul., 2013.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Procesamiento de SeñalesMultimodal graphical models via Group LassoPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaHariri, AhamdMusé, PabloFiori, MarceloSapiro, GuillermoProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | Multimodal graphical models via Group Lasso Hariri, Ahamd Procesamiento de Señales |
| status_str | publishedVersion |
| title | Multimodal graphical models via Group Lasso |
| title_full | Multimodal graphical models via Group Lasso |
| title_fullStr | Multimodal graphical models via Group Lasso |
| title_full_unstemmed | Multimodal graphical models via Group Lasso |
| title_short | Multimodal graphical models via Group Lasso |
| title_sort | Multimodal graphical models via Group Lasso |
| topic | Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/41761 |
