Labeled examples are scarce while there are numerous unlabeled examples in real-world. Manual labeling these unlabeled examples is often expensive and inefficient. Active learning paradigm seeks to handle this problem by identifying the most informative examples from the unlabeled examples to label. In this paper, we present two novel active learning approaches based on non-parallel support vector machines and twin support vector machines which adopt the margin sampling method and the manifold-preserving graph reduction algorithm to select the most informative examples. The manifold-preserving graph reduction is a sparse subset selecting algorithm which exploits the structural space connectivity and spatial diversity among examples. In each iteration, an active learner draws the informative and representative candidates from the subset instead of the whole unlabeled data. This strategy can keep the manifold structure and reduce noisy points and outliers in the whole unlabeled data. Experimental results on multiple datasets validate the effective performance of the proposed methods.

标记的示例很少，而现实世界中有许多未标记的示例。手动标记这些未标记的示例通常很昂贵且效率低下。主动学习范式试图通过从未标记的示例到要标记的示例中识别信息最多的示例来处理此问题。在本文中，我们提出了两种基于非并行支持向量机和双支持向量机的主动学习方法，它们采用余量采样方法和流形保留图约简算法来选择信息量最大的实例。流形保留图约简是一种稀疏子集选择算法，该算法利用示例之间的结构空间连通性和空间多样性。在每次迭代中 积极的学习者从子集中而不是整个未标记的数据中抽取信息丰富且具有代表性的候选人。这种策略可以保持流形结构并减少整个未标记数据中的噪声点和离群值。在多个数据集上的实验结果验证了所提出方法的有效性能。