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Re-revisiting Learning on Hypergraphs: Confidence Interval, Subgradient Method and Extension to Multiclass
IEEE Transactions on Knowledge and Data Engineering ( IF 8.9 ) Pub Date : 2020-03-01 , DOI: 10.1109/tkde.2018.2880448 Chenzi Zhang , Shuguang Hu , Zhihao Gavin Tang , T-H. Hubert Chan
IEEE Transactions on Knowledge and Data Engineering ( IF 8.9 ) Pub Date : 2020-03-01 , DOI: 10.1109/tkde.2018.2880448 Chenzi Zhang , Shuguang Hu , Zhihao Gavin Tang , T-H. Hubert Chan
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.
中文翻译:
重温超图学习:置信区间、次梯度方法和多类扩展
我们重新审视超图的半监督学习。与之前的方法一样,我们的方法使用凸程序,其目标函数并非处处可微。我们利用最优解的非唯一性,并考虑置信区间,该区间给出未标记顶点在任何最优解中的精确范围。此外,我们给出了一种更简单的方法来解决基于次梯度方法的凸程序。我们在真实世界数据集上的实验证实,我们在超图上的置信区间方法优于现有方法,并且当顶点数远大于边数时,我们的次梯度方法可以提供更快的运行时间。我们的实验还支持使用有向超图来捕获因果关系可以提高预测准确性。此外,
更新日期:2020-03-01
中文翻译:
重温超图学习:置信区间、次梯度方法和多类扩展
我们重新审视超图的半监督学习。与之前的方法一样,我们的方法使用凸程序,其目标函数并非处处可微。我们利用最优解的非唯一性,并考虑置信区间,该区间给出未标记顶点在任何最优解中的精确范围。此外,我们给出了一种更简单的方法来解决基于次梯度方法的凸程序。我们在真实世界数据集上的实验证实,我们在超图上的置信区间方法优于现有方法,并且当顶点数远大于边数时,我们的次梯度方法可以提供更快的运行时间。我们的实验还支持使用有向超图来捕获因果关系可以提高预测准确性。此外,