This article tackles the problem of multilabel learning with missing labels. For this problem, it is widely accepted that label correlations can be used to recover the ground-truth label matrix. Most of the existing approaches impose the low-rank assumption on the observed label matrix to exploit label correlations by decomposing it into two matrices, which describe the latent factors of instances and labels, respectively. The quality of these latent factors highly influences the recovery of ground-truth labels and the construction of the multilabel classification model. In this article, we propose recovering the ground-truth label matrix by regularized matrix factorization. Specifically, the latent factors of instances are regularized by the local topological structure derived from the feature space, which can be further used to induce an effective multilabel model. Moreover, the latent factors of labels and the label correlations are mutually adapted via label manifold regularization. In this way, the recovery of the ground-truth label matrix and the construction of the multilabel classification model are optimized jointly and can benefit from the regularized matrix factorization. Extensive experimental studies show that the proposed approach significantly outperforms the state-of-the-art algorithms on both full-label and missing-label data.

中文翻译：

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用于缺失标签的多标签学习的正则化矩阵分解


本文解决了缺少标签的多标签学习问题。对于这个问题，人们普遍认为可以使用标签相关性来恢复真实标签矩阵。大多数现有方法对观察到的标签矩阵施加低秩假设，通过将其分解为两个矩阵来利用标签相关性，这两个矩阵分别描述实例和标签的潜在因素。这些潜在因素的质量很大程度上影响真实标签的恢复和多标签分类模型的构建。在本文中，我们建议通过正则化矩阵分解来恢复真实标签矩阵。具体来说，实例的潜在因子通过从特征空间导出的局部拓扑结构进行正则化，这可以进一步用于诱导有效的多标签模型。此外，标签的潜在因子和标签相关性通过标签流形正则化相互适应。这样，地面真实标签矩阵的恢复和多标签分类模型的构建被联合优化，并且可以受益于正则化矩阵分解。大量的实验研究表明，所提出的方法在全标签和缺失标签数据上都显着优于最先进的算法。