Abstract Weakly-supervised multi-label learning has emerged as a hot topic more recently. Most existing methods deal with such problem by learning from the data where the label assignments are incomplete while the feature information is ideal. However, in many real applications, due to the influence of occlusion, illumination and low-resolution, the acquired features are often noisy, which may reduce the robustness of the learning model. In this paper, to overcome the above shortcoming, we propose a novel weakly-supervised multi-label learning framework called WML-LSC, where the low-rank and sparse constrain schemes are jointly incorporated to capture the desired feature information. Specifically, we first decompose the observed feature matrix into an ideal feature matrix and an outlier matrix. Considering that similar instances usually share similar visual characteristics, we constrain the ideal feature matrix to be low-rank. Meanwhile, a reasonable assumption is that the noise is sparse compared with the feature matrix, which leads outlier matrix to be sparse. In addition, a linear self-recovery model is adopted to reconstruct the incomplete label assignment matrix by exploiting label correlations. Finally, the desired model is trained on the ideal feature matrix and the refined label matrix. Extensive experimental results demonstrate that our proposed method can achieve superior and comparable performance against state-of-the-art methods.

中文翻译：

---

具有噪声特征和不完整标签的弱监督多标签学习

摘要 弱监督多标签学习最近成为一个热门话题。大多数现有方法通过从标签分配不完整而特征信息理想的数据中学习来处理此类问题。然而，在许多实际应用中，由于遮挡、光照和低分辨率的影响，获取的特征往往带有噪声，这可能会降低学习模型的鲁棒性。在本文中，为了克服上述缺点，我们提出了一种称为 WML-LSC 的新型弱监督多标签学习框架，其中联合使用低秩和稀疏约束方案来捕获所需的特征信息。具体来说，我们首先将观察到的特征矩阵分解为理想特征矩阵和离群点矩阵。考虑到相似的实例通常具有相似的视觉特征，我们将理想的特征矩阵限制为低秩。同时，一个合理的假设是噪声相对于特征矩阵是稀疏的，这导致离群点矩阵是稀疏的。此外，通过利用标签相关性，采用线性自恢复模型来重建不完整的标签分配矩阵。最后，在理想特征矩阵和细化标签矩阵上训练所需模型。大量的实验结果表明，我们提出的方法可以实现与最先进方法相比的优越和可比的性能。这导致离群值矩阵稀疏。此外，通过利用标签相关性，采用线性自恢复模型来重建不完整的标签分配矩阵。最后，在理想特征矩阵和细化标签矩阵上训练所需模型。大量的实验结果表明，我们提出的方法可以实现与最先进方法相比的优越和可比的性能。这导致离群值矩阵稀疏。此外，通过利用标签相关性，采用线性自恢复模型来重建不完整的标签分配矩阵。最后，在理想特征矩阵和细化标签矩阵上训练所需模型。大量的实验结果表明，我们提出的方法可以实现与最先进方法相比的优越和可比的性能。