Context-Guided Diffusion for Label Propagation on Graphs
Abstract
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regularizer. Inspired by the success of diffusivity tensors for anisotropic diffusion in image processing, we presents anisotropic diffusion on graphs and the corresponding label propagation algorithm. We develop positive definite diffusivity operators on the vector bundles of Riemannian manifolds, and discretize them to diffusivity operators on graphs. This enables us to easily define new robust diffusivity operators which significantly improve semi-supervised learning performance over existing diffusion algorithms.
Cite
Text
Kim et al. "Context-Guided Diffusion for Label Propagation on Graphs." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.318Markdown
[Kim et al. "Context-Guided Diffusion for Label Propagation on Graphs." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/kim2015iccv-contextguided/) doi:10.1109/ICCV.2015.318BibTeX
@inproceedings{kim2015iccv-contextguided,
title = {{Context-Guided Diffusion for Label Propagation on Graphs}},
author = {Kim, Kwang In and Tompkin, James and Pfister, Hanspeter and Theobalt, Christian},
booktitle = {International Conference on Computer Vision},
year = {2015},
doi = {10.1109/ICCV.2015.318},
url = {https://mlanthology.org/iccv/2015/kim2015iccv-contextguided/}
}