Name
Abstract | ||
|---|---|---|
Both data augmentation and contrastive loss are the key components of contrastive learning. In this paper, we design a new multi-view unsupervised graph representation learning method including adaptive data augmentation and multi-view contrastive learning, to address some issues of contrastive learning ignoring the information from feature space. Specifically, the adaptive data augmentation first builds a feature graph from the feature space, and then designs a deep graph learning model on the original representation and the topology graph to update the feature graph and the new representation. As a result, the adaptive data augmentation outputs multi-view information, which is fed into two GCNs to generate multi-view embedding features. Two kinds of contrastive losses are further designed on multi-view embedding features to explore the complementary information among the topology and feature graphs. Additionally, adaptive data augmentation and contrastive learning are embedded in a unified framework to form an end-to-end model. Experimental results verify the effectiveness of our proposed method, compared to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.24963/ijcai.2022/414 | European Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Machine Learning: Unsupervised Learning,Machine Learning: Multi-view learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangzhang Gan | 1 | 13 | 2.50 |
| Rongyao Hu | 2 | 243 | 14.01 |
| Mengmeng Zhan | 3 | 0 | 0.34 |
| Yujie Mo | 4 | 0 | 0.68 |
| Yingying Wan | 5 | 0 | 0.34 |
| Xiaofeng Zhu | 6 | 0 | 0.34 |