Regularized Mixed Dimensionality and Density Learning in Computer Vision
Abstract
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.
Cite
Text
Haro et al. "Regularized Mixed Dimensionality and Density Learning in Computer Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383401Markdown
[Haro et al. "Regularized Mixed Dimensionality and Density Learning in Computer Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/haro2007cvpr-regularized/) doi:10.1109/CVPR.2007.383401BibTeX
@inproceedings{haro2007cvpr-regularized,
title = {{Regularized Mixed Dimensionality and Density Learning in Computer Vision}},
author = {Haro, Gloria and Randall, Gregory and Sapiro, Guillermo},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2007},
doi = {10.1109/CVPR.2007.383401},
url = {https://mlanthology.org/cvpr/2007/haro2007cvpr-regularized/}
}