Dynamic Label Propagation for Semi-Supervised Multi-Class Multi-Label Classification
Abstract
In graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label classification scheme, dynamic label propagation (DLP), which performs transductive learning through propagation in a dynamic process. Existing semi-supervised classification methods often have difficulty in dealing with multi-class/multi-label problems due to the lack in consideration of label correlation; our algorithm instead emphasizes dynamic metric fusion with label information. Significant improvement over the state-of-the-art methods is observed on benchmark datasets for both multiclass and multi-label tasks.
Cite
Text
Wang et al. "Dynamic Label Propagation for Semi-Supervised Multi-Class Multi-Label Classification." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.60Markdown
[Wang et al. "Dynamic Label Propagation for Semi-Supervised Multi-Class Multi-Label Classification." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/wang2013iccv-dynamic/) doi:10.1109/ICCV.2013.60BibTeX
@inproceedings{wang2013iccv-dynamic,
title = {{Dynamic Label Propagation for Semi-Supervised Multi-Class Multi-Label Classification}},
author = {Wang, Bo and Tu, Zhuowen and Tsotsos, John K.},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.60},
url = {https://mlanthology.org/iccv/2013/wang2013iccv-dynamic/}
}