Co-GCN for Multi-View Semi-Supervised Learning
Abstract
In many real-world applications, the data have several disjoint sets of features and each set is called as a view. Researchers have developed many multi-view learning methods in the past decade. In this paper, we bring Graph Convolutional Network (GCN) into multi-view learning and propose a novel multi-view semi-supervised learning method Co-GCN by adaptively exploiting the graph information from the multiple views with combined Laplacians. Experimental results on real-world data sets verify that Co-GCN can achieve better performance compared with state-of-the-art multi-view semi-supervised methods.
Cite
Text
Li et al. "Co-GCN for Multi-View Semi-Supervised Learning." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I04.5901Markdown
[Li et al. "Co-GCN for Multi-View Semi-Supervised Learning." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/li2020aaai-co/) doi:10.1609/AAAI.V34I04.5901BibTeX
@inproceedings{li2020aaai-co,
title = {{Co-GCN for Multi-View Semi-Supervised Learning}},
author = {Li, Shu and Li, Wen-Tao and Wang, Wei},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {4691-4698},
doi = {10.1609/AAAI.V34I04.5901},
url = {https://mlanthology.org/aaai/2020/li2020aaai-co/}
}