关键词: 多标记学习, 共享子空间, 类别相关性

Abstract: In the multi-label classification problems, different labels corresponding with the same instance may have some correlations, and the label correlation can provide some important information. Some multi-label learning methods are presented to combine the label correlation, but they are built on the original data which are high-dimensional and noisy, then they cannot obtain the satisfied results. This paper proposes a common subspace based multi-label learning method to extract high-level information from the original feature space by making use of the label information. Based on the feature mapping function, the original high-dimensional data can be effectively represented in the common low-dimensional subspace. Meanwhile, the proposed method also learns the mapping function from the label space to the common high-level feature space, then the label information of instances can be obtained with the new representation in the subspace. The experimental results on five real-world data sets show that the model can effectively improve the performance of multi-label classification.

Key words: multi-label learning, common subspace, label correlation