Learning implicit labeling-importance and label correlation for multi-label feature selection with streaming labels

Jinghua Liu, Wei Wei, Yaojin Lin, Lijie Yang, Hongbo Zhang

Abstract: Highlights•By jointly learning the implicit labeling-importance for streaming labels and the impact of label correlation on feature importance, a novel framework is established to figure out multi-label feature selection with dynamic streaming labels.•The implicit labeling-importance for dynamic streaming labels is excavated by utilizing the neighborhood structure of samples on feature space, thereby providing valuable additional information for the learning task.•Label correlation is leveraged to advance generalization performance, especially, the effect degree of the already-arrived labels on the relationship between features and the newly-arrived labels is quantified to facilitate the model training.