Zhang Changqing, Yu Ziwei, Fu Huazhu, Zhu Pengfei, Chen Lei, Hu Qinghua
IEEE Trans Cybern. 2020 Jun;50(6):2837-2850. doi: 10.1109/TCYB.2019.2894985. Epub 2019 Feb 11.
For real-world applications, multilabel learning usually suffers from unsatisfactory training data. Typically, features may be corrupted or class labels may be noisy or both. Ignoring noise in the learning process tends to result in an unreasonable model and, thus, inaccurate prediction. Most existing methods only consider either feature noise or label noise in multilabel learning. In this paper, we propose a unified robust multilabel learning framework for data with hybrid noise, that is, both feature noise and label noise. The proposed method, hybrid noise-oriented multilabel learning (HNOML), is simple but rather robust for noisy data. HNOML simultaneously addresses feature and label noise by bi-sparsity regularization bridged with label enrichment. Specifically, the label enrichment matrix explores the underlying correlation among different classes which improves the noisy labeling. Bridged with the enriching label matrix, the structured sparsity is imposed to jointly handle the corrupted features and noisy labeling. We utilize the alternating direction method (ADM) to efficiently solve our problem. Experimental results on several benchmark datasets demonstrate the advantages of our method over the state-of-the-art ones.
对于实际应用，多标签学习通常受到训练数据不理想的困扰。通常，特征可能被损坏，或者类别标签可能有噪声，或者两者皆有。在学习过程中忽略噪声往往会导致不合理的模型，从而导致预测不准确。大多数现有方法在多标签学习中只考虑特征噪声或标签噪声。在本文中，我们针对具有混合噪声（即特征噪声和标签噪声）的数据提出了一个统一的鲁棒多标签学习框架。所提出的方法，即面向混合噪声的多标签学习（HNOML），简单但对噪声数据相当鲁棒。HNOML通过与标签丰富化相结合的双稀疏正则化同时处理特征噪声和标签噪声。具体而言，标签丰富化矩阵探索不同类别之间的潜在相关性，从而改善有噪声的标注。与丰富化标签矩阵相结合，施加结构化稀疏性以联合处理损坏的特征和有噪声的标注。我们利用交替方向法（ADM）有效地解决我们的问题。在几个基准数据集上的实验结果证明了我们的方法优于现有最先进方法的优势。
