In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously. Generally, all relevant labels are considered to be available for labeled data. However, instances with a full label set are difficult to obtain in real-world applications, thus leading to the weakly multilabel learning problem, that is, relevant labels of training data are partially known and many relevant labels are missing, and even abundant training data are associated with an empty label set. To address the problem, we propose a new multilabel method to learn from weakly labeled data. To be specific, an optimization framework is constructed based on the manifold regularized sparse model, in which the correlations among labels and feature structure are considered to model global and local label correlations, thereby achieving discriminative feature analysis for mapping training data to ground-truth label space. Moreover, the proposed method has an excellent mechanism to conduct semisupervised multilabel learning by exploiting training data with the predicted label set of the unlabeled. Experiments on various real-world tasks reveal that the proposed method outperforms some state-of-the-art methods.

中文翻译：

---

基于流形正则化稀疏模型的弱标记数据学习

在多标签学习中，每个训练示例都由一个实例表示，该实例同时与多个类标签相关。通常，所有相关标签都被认为可用于标记数据。然而，在实际应用中很难获得具有完整标签集的实例，从而导致弱多标签学习问题，即训练数据的相关标签部分已知，许多相关标签丢失，甚至是丰富的训练数据与空标签集相关联。为了解决这个问题，我们提出了一种新的多标签方法来从弱标签数据中学习。具体来说，基于流形正则化稀疏模型构建优化框架，其中标签和特征结构之间的相关性被认为是对全局和局部标签相关性进行建模，从而实现将训练数据映射到真实标签空间的判别特征分析。此外，所提出的方法具有通过利用具有未标记的预测标签集的训练数据来进行半监督多标签学习的优秀机制。对各种现实世界任务的实验表明，所提出的方法优于一些最先进的方法。