Título:
Regularized mixed dimensionality and density learning in computer vision
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Autor:
Randall, Gregory
Haro, Gloria
Sapiro, Guillermo
Haro, Gloria
Sapiro, Guillermo
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Tipo de documento:
Preprint
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Año de publicación:
2007
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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.
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Trabajo presentado en IEEE Conference on Computer Vision and Pattern Recognition, 2007.
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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
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| HRS07.pdf | — | 795.18 KB | Adobe PDF |