Graph-Based Semi-Supervised Learning: Realizing Pointwise Smoothness Probabilistically
Abstract
As the central notion in semi-supervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graph-based work exploits them in conjunction, we encompass both in a novel framework of Probabilistic Graph-based Pointwise Smoothness (PGP), building upon two foundational models of data closeness and label coupling. This new form of smoothness axiomatizes a set of probability constraints, which ultimately enables class prediction. Theoretically, we provide an error and robustness analysis of PGP. Empirically, we conduct extensive experiments to show the advantages of PGP.
Cite
Text
Fang et al. "Graph-Based Semi-Supervised Learning: Realizing Pointwise Smoothness Probabilistically." International Conference on Machine Learning, 2014.Markdown
[Fang et al. "Graph-Based Semi-Supervised Learning: Realizing Pointwise Smoothness Probabilistically." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/fang2014icml-graphbased/)BibTeX
@inproceedings{fang2014icml-graphbased,
title = {{Graph-Based Semi-Supervised Learning: Realizing Pointwise Smoothness Probabilistically}},
author = {Fang, Yuan and Chang, Kevin and Lauw, Hady},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {406-414},
volume = {32},
url = {https://mlanthology.org/icml/2014/fang2014icml-graphbased/}
}