关键词: 多标记学习, 标记相关性, 余弦相似性

Abstract: Multi-label learning deals with the problem where each instance has a set of class labels simultaneously. In multi-label learning, label correlations have shown promising strength in improving multi-label learning. Most of the existing multi-label learning algorithms exploited either global label correlations shared among all instances, or local label correlations varied across different clusters of instances. In this study, we propose a novel multi-label learning method by simultaneously taking into account both the global and local label correlations to capture more comprehensive label information during the learning process. To calculating global and local label correlations, we utilize cosine similarity to obtain positive and negative correlations between different labels, which helps us to further achieve more reliable multi-label learning. We implemented extensive experimental comparisons based on various data sets to validate the effectiveness of our algorithm. The experimental results show that the proposed algorithm significantly outperforms most of the state-of-the art approaches, demonstrating its prominent performance for multi-label learning.

Key words: multi-label learning, label correlations, cosine similarity