Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping
Abstract
Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled. When the labelling status depends on the unobserved node response, ignoring the missingness can lead to significant estimation bias and handicap the classifiers. This situation is called non-ignorable non-response. To solve the problem, we propose a Graph-based joint model with Non-ignorable Non-response (GNN), followed by a joint inverse weighting estimation procedure incorporated with sampling imputation approach. Our method is proved to outperform some state-of-art models in both regression and classification problems, by simulations and real analysis on the Cora dataset.
BibTeX
@inproceedings{NEURIPS2019_42547f5a,
author = {Zhou, Fan and Li, Tengfei and Zhou, Haibo and Zhu, Hongtu and Jieping, Ye},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Graph-Based Semi-Supervised Learning with Non-ignorable Non-response},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/42547f5a44d87da3bc40ee5d09624606-Paper.pdf},
volume = {32},
year = {2019}
}