Online Robust Dictionary Learning
Abstract
Online dictionary learning is particularly useful for processing large-scale and dynamic data in computer vision. It, however, faces the major difficulty to incorporate robust functions, rather than the square data fitting term, to handle outliers in training data. In this paper, we propose a new online framework enabling the use of ersparse data fitting term in robust dictionary learning, notably enhancing the usability and practicality of this important technique. Extensive experiments have been carried out to validate our new framework.
Cite
Text
Lu et al. "Online Robust Dictionary Learning." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.60Markdown
[Lu et al. "Online Robust Dictionary Learning." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/lu2013cvpr-online/) doi:10.1109/CVPR.2013.60BibTeX
@inproceedings{lu2013cvpr-online,
title = {{Online Robust Dictionary Learning}},
author = {Lu, Cewu and Shi, Jiaping and Jia, Jiaya},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2013},
doi = {10.1109/CVPR.2013.60},
url = {https://mlanthology.org/cvpr/2013/lu2013cvpr-online/}
}