COS-LDL: Label Distribution Learning by Cosine-Based Distance-Mapping Correlation

Label distribution learning (LDL) is a popular research trend in multi-label learning. Competing methods have been designed to improve the predictive performance. In this paper, we propose a method called cosine-based correlation for LDL (COS-LDL). The key issue is how to exploit correlations among different labels of the same instance. We propose a distance-mapping function for this purpose. With this mapping function, we design an objective function and its corresponding learning algorithm. Experiments undertaken on thirteen real-world datasets compare with eight LDL state-of-the-art methods. Results demonstrate that COS-LDL outperforms them in eight out of ten popular measures.