In partial label learning, a multi-class classifier is learned from the ambiguous supervision where each training example is associated with a set of candidate labels among which only one is valid. An intuitive way to deal with this problem is label disambiguation, i.e., differentiating the labeling confidences of different candidate labels so as to try to recover ground-truth labeling information. Recently, feature-aware label disambiguation has been proposed which utilizes the graph structure of feature space to generate labeling confidences over candidate labels. Nevertheless, the existence of noises and outliers in training data makes the graph structure derived from original feature space less reliable. In this paper, a novel partial label learning approach based on adaptive graph guided disambiguation is proposed, which is shown to be more effective in revealing the intrinsic manifold structure among training examples. Other than the sequential disambiguation-then-induction learning strategy, the proposed approach jointly performs adaptive graph construction, candidate label disambiguation and predictive model induction with alternating optimization. Furthermore, we consider the particular human-in-the-loop framework in which a learner is allowed to actively query some ambiguously labeled examples for manual disambiguation. Extensive experiments clearly validate the effectiveness of adaptive graph guided disambiguation for learning from partial label examples.

中文翻译：

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部分标签学习的自适应图引导消歧。

在部分标签学习中，多类分类器是从模糊监督中学习的，其中每个训练示例与一组候选标签相关联，其中只有一个是有效的。处理这个问题的一种直观方法是标签消歧，即区分不同候选标签的标签置信度，以尝试恢复真实标签信息。最近，已经提出了特征感知标签消歧，它利用特征空间的图结构来生成候选标签的标签置信度。然而，训练数据中噪声和异常值的存在使得从原始特征空间导出的图结构不太可靠。在本文中，提出了一种基于自适应图引导消歧的新型局部标签学习方法，这被证明可以更有效地揭示训练示例中的内在流形结构。除了顺序消歧然后归纳学习策略外，所提出的方法联合执行自适应图构建、候选标签消歧和预测模型归纳与交替优化。此外，我们考虑了特定的人在循环框架中，在该框架中，允许学习者主动查询一些标记不明确的示例以进行手动消歧。大量实验清楚地验证了自适应图引导消歧从部分标签示例中学习的有效性。所提出的方法通过交替优化联合执行自适应图构建、候选标签消歧和预测模型归纳。此外，我们考虑了特定的人在循环框架中，在该框架中，允许学习者主动查询一些标记不明确的示例以进行手动消歧。大量实验清楚地验证了自适应图引导消歧从部分标签示例中学习的有效性。所提出的方法通过交替优化联合执行自适应图构建、候选标签消歧和预测模型归纳。此外，我们考虑了特定的人在循环框架中，在该框架中，允许学习者主动查询一些标记不明确的示例以进行手动消歧。大量实验清楚地验证了自适应图引导消歧从部分标签示例中学习的有效性。