Re-Revisiting Learning on Hypergraphs: Confidence Interval, Subgradient Method, and Extension to Multiclass
| Autor: | Chenzi Zhang, T-H. Hubert Chan, Shuguang Hu, Zhihao Gavin Tang |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Science::Machine Learning
Linear programming Computer science Supervised learning 02 engineering and technology Semi-supervised learning Directed graph Computer Science Applications Computational Theory and Mathematics 020204 information systems Convex optimization 0202 electrical engineering electronic engineering information engineering Differentiable function Subgradient method Algorithm Information Systems |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 32:506-518 |
| ISSN: | 2326-3865 1041-4347 |
| DOI: | 10.1109/tkde.2018.2880448 |
| Popis: | We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confidence intervals which give the exact ranges that unlabeled vertices take in any optimal solution. Moreover, we give a much simpler approach for solving the convex program based on the subgradient method. Our experiments on real-world datasets confirm that our confidence interval approach on hypergraphs outperforms existing methods, and our subgradient method gives faster running times when the number of vertices is much larger than the number of edges. Our experiments also support that using directed hypergraphs to capture causal relationships can improve the prediction accuracy. Furthermore, our model can be readily extended to capture multiclass learning. |
| Databáze: | OpenAIRE |
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