Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 21-26 June 2014
City or Country
FANG, Yuan; CHANG, Kevin Chen-Chuan; and LAUW, Hady Wirawan.
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically. (2014). Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 21-26 June 2014. 1-9. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2249
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.