Partial multilabel learning (PML) aims to learn from training data, where each instance is associated with a set of candidate labels, among which only a part is correct. The common strategy to deal with such a problem is disambiguation, that is, identifying the ground-truth labels from the given candidate labels. However, the existing PML approaches always focus on leveraging the instance relationship to disambiguate the given noisy label space, while the potentially useful information in label space is not effectively explored. Meanwhile, the existence of noise and outliers in training data also makes the disambiguation operation less reliable, which inevitably decreases the robustness of the learned model. In this article, we propose a prior label knowledge regularized self-representation PML approach, called PAKS, where the self-representation scheme and prior label knowledge are jointly incorporated into a unified framework. Specifically, we introduce a self-representation model with a low-rank constraint, which aims to learn the subspace representations of distinct instances and explore the high-order underlying correlation among different instances. Meanwhile, we incorporate prior label knowledge into the above self-representation model, where the prior label knowledge is regarded as the complement of features to obtain an accurate self-representation matrix. The core of PAKS is to take advantage of the data membership preference, which is derived from the prior label knowledge, to purify the discovered membership of the data and accordingly obtain more representative feature subspace for model induction. Enormous experiments on both synthetic and real-world datasets show that our proposed approach can achieve superior or comparable performance to state-of-the-art approaches.

中文翻译：

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用于部分多标签学习的先验知识正则化自我表示模型


部分多标签学习（PML）旨在从训练数据中学习，其中每个实例都与一组候选标签相关联，其中只有一部分是正确的。处理此类问题的常见策略是消歧，即从给定的候选标签中识别真实标签。然而，现有的 PML 方法始终专注于利用实例关系来消除给定的噪声标签空间的歧义，而没有有效地探索标签空间中潜在有用的信息。同时，训练数据中噪声和异常值的存在也使得消歧操作不太可靠，这不可避免地降低了学习模型的鲁棒性。在本文中，我们提出了一种先验标签知识正则化自表示 PML 方法，称为 PAKS，其中将自表示方案和先验标签知识共同纳入一个统一的框架中。具体来说，我们引入了一种具有低秩约束的自表示模型，其目的是学习不同实例的子空间表示并探索不同实例之间的高阶潜在相关性。同时，我们将先验标签知识纳入上述自表示模型中，将先验标签知识视为特征的补充，以获得准确的自表示矩阵。 PAKS的核心是利用源自先验标签知识的数据成员偏好来纯化已发现的数据成员，从而获得更具代表性的特征子空间用于模型归纳。 对合成数据集和现实数据集的大量实验表明，我们提出的方法可以实现比最先进的方法优越或可比的性能。