增强学习标签相关性的多标签特征选择方法.

Aiming at two problems of existing multi-label feature selection methods: first, ignoring the influence of noise information in the process of learning label correlations; second, neglecting to explore the comprehensive label information of each cluster, the paper proposed a multi-label feature selection method that enhanced label correlation learning. Initially, it clustered the samples and treated each cluster center as a representative instance of the comprehensive semantic information of the samples, while computing its corresponding label vectors which reflected the importance of different labels contained in each cluster. Then, through the label-level self-representation of the original samples and the center of each cluster, it both captured the label correlations in the original label space, and explored the label correlations within each cluster. Finally, the self-representation coefficient matrix was sparse to reduce the effect of noise, and the original sample and the representative instance of each cluster were mapped from the feature space to the reconstructed label space for feature selection. Experimental results on nine multi-labeled datasets show that the proposed algorithm has better performance compared with other methods. [ABSTRACT FROM AUTHOR]