Moving Window Regression: A Novel Approach to Ordinal Regression
Abstract
A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (rho-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (rho-regressors) to predict rho-ranks within entire and specific rank ranges, respectively. Third, we refine an initial rank estimate iteratively by selecting two reference instances to form a search window and then estimating the rho-rank within the window. Extensive experiments results show that the proposed algorithm achieves the state-of-the-art performances on various benchmark datasets for facial age estimation and historical color image classification. The codes are available at https://github.com/nhshin-mcl/MWR.
Cite
Text
Shin et al. "Moving Window Regression: A Novel Approach to Ordinal Regression." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.01820Markdown
[Shin et al. "Moving Window Regression: A Novel Approach to Ordinal Regression." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/shin2022cvpr-moving/) doi:10.1109/CVPR52688.2022.01820BibTeX
@inproceedings{shin2022cvpr-moving,
title = {{Moving Window Regression: A Novel Approach to Ordinal Regression}},
author = {Shin, Nyeong-Ho and Lee, Seon-Ho and Kim, Chang-Su},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2022},
pages = {18760-18769},
doi = {10.1109/CVPR52688.2022.01820},
url = {https://mlanthology.org/cvpr/2022/shin2022cvpr-moving/}
}