Yunbin Wang, Na Song, Shiping Wang, Unsupervised sample selection for active learning with quadratic programming
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DOI: 10.23952/jano.6.2024.3.03
Volume 6, Issue 3, 1 December 2024, Pages 339-350
Abstract. Graph Neural Networks (GNNs), which gained popularity recently, is facing the problem of reducing the cost of acquiring large datasets. Although a portion of the work combining GNN with active learning has been moderately successful, there are still some shortcomings in this research area. Most of the studies using clustering methods can only obtain local optima, and some of them suffer from the problem of difficult convergence. Moreover, the result of clustering is often undesired if the amount of data in each class is not balanced. Some methods obtain higher performance by combining multiple metrics, but are limited by the adverse effects of under-training the initial network. For these reasons, we propose an active learning framework based on quadratic programming in this paper. This framework transforms the sample sampling process into an optimal solution problem, which can obtain the global optimal solution and avoid the problem of hard convergence. Experimental results on several datasets demonstrate that the proposed method outperforms other baselines.
How to Cite this Article:
Y. Wang, N. Song, S. Wang, Unsupervised sample selection for active learning with quadratic programming, J. Appl. Numer. Optim. 6 (2024), 339-350.