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

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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 paper, 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 learning 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.
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Dates and versions

hal-03100039 , version 1 (06-01-2021)

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  • HAL Id : hal-03100039 , version 1

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Mariana Vargas-Vieyra, Aurélien Bellet, Pascal Denis. Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning. 14th Workshop on Graph-Based Natural Language Processing (TextGraphs 2020), 2020, Virtual, Spain. ⟨hal-03100039⟩
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