Multi-label learning deals with the problem where each instance is associated with a set of class labels. In multi-label learning, different labels may have their own inherent characteristics for distinguishing each other, and the correlation information has shown promising strength in improving multi-label learning. In this study, we propose a novel multi-label learning method by simultaneously taking into account both the learning of label-specific features and the correlation information during the learning process. Firstly, we learn a sparse weight parameter vector for each label based on the linear regression model, and the label-specific features can be extracted according to the corresponding weight parameters. Secondly, we constrain label correlations directly on the output of labels, not on the corresponding parameter vectors which conflicts with the label-specific feature learning. Specifically, for any two related labels, their corresponding models should have similar outputs rather than similar parameter vectors. Thirdly, we also exploit the sample correlations through sparse reconstruction. The experimental results on 12 benchmark datasets show that the proposed method performs better than the existing methods. The proposed method ranks in the 1st place at 66.7% case and achieves optimal average rank in terms of all evaluation measures.

中文翻译：

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多标签学习的联合标签特定特征和相关信息

多标签学习处理每个实例与一组类标签相关联的问题。在多标签学习中，不同的标签可能有其固有的区分特征，相关信息在改进多标签学习方面显示出了很大的潜力。在这项研究中，我们通过在学习过程中同时考虑标签特定特征的学习和相关信息，提出了一种新的多标签学习方法。首先，我们基于线性回归模型为每个标签学习一个稀疏的权重参数向量，并根据相应的权重参数提取标签特有的特征。其次，我们直接在标签的输出上约束标签相关性，不在与标签特定特征学习冲突的相应参数向量上。具体来说，对于任何两个相关的标签，它们对应的模型应该有相似的输出而不是相似的参数向量。第三，我们还通过稀疏重建来利用样本相关性。在 12 个基准数据集上的实验结果表明，该方法的性能优于现有方法。所提出的方法在 66.7% 的案例中排名第一，并且在所有评估措施方面都达到了最佳平均排名。在 12 个基准数据集上的实验结果表明，该方法的性能优于现有方法。所提出的方法在 66.7% 的案例中排名第一，并且在所有评估措施方面都达到了最佳平均排名。在 12 个基准数据集上的实验结果表明，该方法的性能优于现有方法。所提出的方法在 66.7% 的案例中排名第一，并且在所有评估措施方面都达到了最佳平均排名。