融合语义差别和流型学习的偏标记学习方法

偏标记学习是一种重要的弱监督学习框架。在偏标记学习中，每个实例与一组候选标记相关联，它的真实标记隐藏在候选标记集合中，且在学习过程中不可获知。为了消除候选标记对学习过程的影响，提出了一种融合实例语义差别最大化和流型学习的偏标记学习方法(partial label learning by semantic difference and manifold learning, PL-SDML)。该方法是一个两阶段的方法：在训练阶段，基于实例的语义差别最大化准则和流型学习方法为训练实例生成标记置信度；在预测阶段，使用基于最近邻投票的方法为未知实例预测标记类别。在四组人工改造的UCI数据集中，在平均70%的情况下优于其他对比算法。在四组真实偏标记数据集中，相比其他对比算法，取得了0.3%～13.8%的性能提升。

Partial label learning by semantic difference and manifold learning

Partial label learning is a weakly supervised learning framework. In partial label learning, each instance is associated with a set of candidate labels, and its ground-truth label is unknown to us during the training process. In order to eliminate the ambiguous of candidate labels, this paper put forward a novel partial label learning by semantic difference and manifold learning(PL-SDML) method, which combined the semantic difference maximization criterion of instances and manifold learning for partial label learning. The PL-SDML method was a two-stage method that uses semantic difference maximization criterion of instances and manifold learning to generate the labeling confidence for training instances in the training phase. Then, PL-SDML made predicts for unseen instances via a nearest neighbor voting-based approach in the predict phase. On the UCI datasets, PL-SDML is superior to other comparison algorithms in 70% cases. On the four real-world datasets, the classification performance of PL-SDML improved by 0.3%~13.8% compared with other baselines.