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GM-PLL: Graph Matching based Partial Label Learning
IEEE Transactions on Knowledge and Data Engineering ( IF 8.9 ) Pub Date : 2021-02-01 , DOI: 10.1109/tkde.2019.2933837
Gengyu Lyu , Songhe Feng , Tao Wang , Congyan Lang , Yidong Li

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate labels. In this paper, we interpret such assignments as instance-to-label matchings, and reformulate the task of PLL as a matching selection problem. To model such problem, we propose a novel Graph Matching based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM) scheme is incorporated owing to its excellent capability of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one GM algorithm does not satisfy the constraint of PLL problem that multiple instances may correspond to the same label, we extend a traditional one-to-one probabilistic matching algorithm to the many-to-one constraint, and make the proposed framework accommodate to the PLL problem. Moreover, we also propose a relaxed matching prediction model, which can improve the prediction accuracy via GM strategy. Extensive experiments on both artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance against the state-of-the-art methods.

中文翻译:

GM-PLL:基于图匹配的部分标签学习

部分标签学习(PLL)旨在从数据中学习,其中每个训练示例都与一组候选标签相关联,其中只有一个是正确的。处理此类问题的关键是消除候选标签集的歧义并获得实例与其候选标签之间的正确分配。在本文中,我们将此类分配解释为实例到标签的匹配,并将 PLL 的任务重新表述为匹配选择问题。为了对此类问题进行建模,我们提出了一种新颖的基于图匹配的部分标签学习(GM-PLL)框架,其中结合了图匹配(GM)方案,因为它具有利用实例和标签关系的出色能力。同时,由于传统的一对一 GM 算法不满足多个实例可能对应同一个标签的 PLL 问题的约束,我们将传统的一对一概率匹配算法扩展到多对一约束,并使得提议的框架适应了 PLL 问题。此外,我们还提出了一种宽松匹配预测模型,该模型可以通过 GM 策略提高预测精度。对人工和现实世界数据集的大量实验表明,与最先进的方法相比,所提出的方法可以实现卓越或相当的性能。可以通过GM策略提高预测精度。对人工和现实世界数据集的大量实验表明,与最先进的方法相比,所提出的方法可以实现卓越或相当的性能。可以通过GM策略提高预测精度。对人工和现实世界数据集的大量实验表明,与最先进的方法相比,所提出的方法可以实现卓越或相当的性能。
更新日期:2021-02-01
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