Improving multi-label classification with missing labels by learning label-specific features

Abstract Existing multi-label learning approaches mainly utilize an identical data representation composed of all the features in the discrimination of all the labels, and assume that all the class labels are observed for each training sample. However, in multi-label learning, each class label might be determined by some specific features of its own, and only a partial label set of each example can be obtained for some real applications. This paper proposes a new method to learn Label-Specific features for multi-label classification with Missing Labels, named LSML. First, a new supplementary label matrix is augmented from the incomplete label matrix by learning high-order label correlations. Then, a label-specific data representation for each class label is learned, and the multi-label classifier is constructed simultaneously based on it by incorporating the learned high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness of the proposed method.