Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning

Mariana Vargas-Vieyra, Aurélien Bellet, Pascal Denis


Abstract
Graph-based semi-supervised learning is appealing when labels are scarce but large amounts of unlabeled data are available. These methods typically use a heuristic strategy to construct the graph based on some fixed data representation, independently of the available labels. In this pa- per, we propose to jointly learn a data representation and a graph from both labeled and unlabeled data such that (i) the learned representation indirectly encodes the label information injected into the graph, and (ii) the graph provides a smooth topology with respect to the transformed data. Plugging the resulting graph and representation into existing graph-based semi-supervised learn- ing algorithms like label spreading and graph convolutional networks, we show that our approach outperforms standard graph construction methods on both synthetic data and real datasets.
Anthology ID:
2020.textgraphs-1.4
Volume:
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
TextGraphs
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–45
Language:
URL:
https://aclanthology.org/2020.textgraphs-1.4
DOI:
10.18653/v1/2020.textgraphs-1.4
Bibkey:
Cite (ACL):
Mariana Vargas-Vieyra, Aurélien Bellet, and Pascal Denis. 2020. Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 35–45, Barcelona, Spain (Online). Association for Computational Linguistics.
Cite (Informal):
Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning (Vargas-Vieyra et al., TextGraphs 2020)
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PDF:
https://aclanthology.org/2020.textgraphs-1.4.pdf