Regularized mixed dimensionality and density learning in computer vision
Resumen:
A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well. The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications.
| 2007 | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/38789 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522935899947008 |
|---|---|
| author | Randall, Gregory |
| author2 | Haro, Gloria Sapiro, Guillermo |
| author2_role | author author |
| author_facet | Randall, Gregory Haro, Gloria Sapiro, Guillermo |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Randall, Gregory Haro, Gloria Sapiro, Guillermo |
| dc.date.accessioned.none.fl_str_mv | 2023-08-01T20:33:47Z |
| dc.date.available.none.fl_str_mv | 2023-08-01T20:33:47Z |
| dc.date.issued.es.fl_str_mv | 2007 |
| dc.date.submitted.es.fl_str_mv | 20230801 |
| dc.description.abstract.none.fl_txt_mv | A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well. The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications. |
| dc.description.es.fl_txt_mv | Trabajo presentado en IEEE Conference on Computer Vision and Pattern Recognition, 2007. |
| dc.identifier.citation.es.fl_str_mv | Randall, G., Haro, G., Sapiro, G. Regularized mixed dimensionality and density learning in computer vision [Preprint] Publicado en Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007. doi 10.1109/CVPR.2007.383401 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/38789 |
| dc.language.iso.none.fl_str_mv | en eng |
| 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.title.none.fl_str_mv | Regularized mixed dimensionality and density learning in computer vision |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | Trabajo presentado en IEEE Conference on Computer Vision and Pattern Recognition, 2007. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_afb4c45d0ddfa207525bfa745212b2a0 |
| identifier_str_mv | Randall, G., Haro, G., Sapiro, G. Regularized mixed dimensionality and density learning in computer vision [Preprint] Publicado en Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007. doi 10.1109/CVPR.2007.383401 |
| 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:20.500.12008/38789 |
| publishDate | 2007 |
| 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 | 2023-08-01T20:33:47Z2023-08-01T20:33:47Z200720230801Randall, G., Haro, G., Sapiro, G. Regularized mixed dimensionality and density learning in computer vision [Preprint] Publicado en Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007. doi 10.1109/CVPR.2007.383401https://hdl.handle.net/20.500.12008/38789Trabajo presentado en IEEE Conference on Computer Vision and Pattern Recognition, 2007.A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well. The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications.Made available in DSpace on 2023-08-01T20:33:47Z (GMT). No. of bitstreams: 5 HRS07.pdf: 814260 bytes, checksum: 39631314a77e0524a1336d9f28469da8 (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4194 bytes, checksum: 7f2e2c17ef6585de66da58d1bfa8b5e1 (MD5) Previous issue date: 2007enengLas 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)Regularized mixed dimensionality and density learning in computer visionPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaRandall, GregoryHaro, GloriaSapiro, 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- Universidad de la Repúblicafalse |
| spellingShingle | Regularized mixed dimensionality and density learning in computer vision Randall, Gregory |
| status_str | submittedVersion |
| title | Regularized mixed dimensionality and density learning in computer vision |
| title_full | Regularized mixed dimensionality and density learning in computer vision |
| title_fullStr | Regularized mixed dimensionality and density learning in computer vision |
| title_full_unstemmed | Regularized mixed dimensionality and density learning in computer vision |
| title_short | Regularized mixed dimensionality and density learning in computer vision |
| title_sort | Regularized mixed dimensionality and density learning in computer vision |
| url | https://hdl.handle.net/20.500.12008/38789 |
