Multi-label feature selection with missing labels

The consistently increasing of the feature dimension brings about great time complexity and storage burden for multi-label learning. Numerous multi-label feature selection techniques are developed to alleviate the effect of high-dimensionality. The existing multi-label feature selection algorithms assume that the labels of the training data are complete. However, this assumption does not always hold true for labeling data is costly and there is ambiguity among classes. Hence, in real-world applications, the data available usually have an incomplete set of labels. In this paper, we present a novel multi-label feature selection model under the circumstance of missing labels. With the proposed algorithm, the most discriminative features are selected and missing labels are recovered simultaneously. To remove the irrelevant and noisy features, the effective l2, p-norm (0 < p ≤ 1) regularization item is imposed on the feature selection matrix. To solve the optimization problem, we developed an iterative reweighted least squares (IRLS) algorithm with guaranteed convergence. Experimental results on benchmark datasets show that the proposed method outperforms the state-of-the-art multi-label feature selection algorithms.