Multi-label feature selection with streaming labels

In this paper, we study a novel and challenging issue, multi-label feature selection with streaming labels, in which the number of labels is unknown in advance, and the size of the feature set is constant. In this problem, we assume that the labels arrive one at a time, and the learning task is to rank features iteratively when a new label arrives. Traditional multi-label feature selection methods cannot perform well in this scenario. Therefore, we present an optimization framework where the weight of each label's feature rank list and the final feature rank list are defined as two sets of unknown variables. The objective is to minimize the overall weighted deviation between the final feature rank list and each label's feature rank list. Extensive experiments on benchmark data sets demonstrate that the proposed method outperforms other multi-label feature selection methods.