Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel regularization approach for instance-based superset label (RegISL) learning so that our instance-based method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy.

中文翻译：

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基于实例的超集标签学习的正则化方法

与传统的监督学习（每个训练示例仅具有一个显式标签）不同，超集标签学习（SLL）是指一个训练示例可以与一组候选标签相关联，并且其中只有一个是正确的问题。现有的SLL方法是基于正则化的或基于实例的，并且后者已实现了最新的性能。这是因为最新的基于实例的方法包含明确的消歧操作，该操作可从歧义的候选标签中准确地拾取每个训练示例的基本标签。但是，这种消歧操作并未充分考虑不同候选标签之间的互斥关系，因此消歧标签通常以非歧视方式生成，这对于基于实例的方法获得令人满意的性能是不利的。为了解决此缺陷，我们开发了一种新颖的基于实例的超集标签（RegISL）学习的正则化方法，从而使我们的基于实例的方法也继承了正则化方案所具有的良好判别能力。具体来说，我们采用图形来表示训练集，并要求图形上相邻的示例才能获得相似的标签。更重要的是，针对每个训练示例，提出了一个区分项来扩大可能的标签和不太可能的标签之间的值差距。结果，部署了不同候选标签之间的固有约束，并且与现有基于实例的算法所输出的标签相比，RegISL生成的已消除歧义的标签更具区分性和准确性。