基于随机子空间的多标签类属特征提取算法

目前多标签学习已广泛应用到很多场景中，在此类学习问题中，一个样本往往可以同时拥有多个类别标签。由于类别标签可能带有的特有属性（即类属属性）将更有助于标签分类，所以已经出现了一些基于类属属性的多标签学习算法。针对类属属性构造会导致属性空间存在冗余的问题，本文提出了一种多标签类属特征提取算法LIFT_RSM。该方法基于类属属性空间通过综合利用随机子空间模型及成对约束降维思想提取有效的特征信息，以达到提升分类性能的目的。在多个数据集上的实验结果表明：与若干经典的多标签算法相比，提出的LIFT_RSM算法能得到更好的分类效果。

Multi-label label-specific feature extraction algorithm based on random subspace

Multi-label learning has been widely used in many application scenarios right now. In this kind of learning problem, each instance is simultaneously assigned with more than one class label. Since different class labels might have their own unique characteristics (i. e. , label-specific feature) which would be more useful for label classification, so some multi-label learning approaches based on label-specific features had already been proposed. Therefore, aiming at the problem that redundant feature space caused by label-specific feature construction, a multi-label label-specific feature extraction algorithm named LIFT_RSM is proposed, which can improve the performance of classification by comprehensively using random subspace method and the thought of pair-wise constraint dimensionality reduction to extract effective feature information in label-specific feature space. The experimental results on several datasets show that the proposed algorithm can achieve better classification results compared with several classical multi-label algorithms.