基于随机子空间的多标签类属特征提取算法

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.