Beyond missing: weakly-supervised multi-label learning with incomplete and noisy labels

摘要

Weakly-supervised multi-label learning has received much attention more recently, and most of the existing methods focus on such problem with either missing or noisy labels, while the issue with both missing and noisy labels has not been well investigated. In this paper, we propose a novel COst-sensitive label Ranking Approach with Low-rank and Sparse constraints (CORALS) to enrich the missing labels and remove the noisy labels simultaneously. Unlike most existing studies that an indicator matrix needs to be given in advance which may not be available in reality, a label confidence matrix is constructed to reflect the relevance between the labels and the corresponding instances, and then the relevance ordering of all possible labels including both missing and noisy labels on each instance is optimized by minimizing a cost-sensitive ranking loss. By considering the dependencies in both feature space and label space, we exploit the dual low-rank regularization terms to capture the corresponding correlations. Afterwards, noticing the fact that both missing and noisy labels are rare, the sparse regularization term is encoded to constrain such noisy information to be sparse. Comprehensive experimental results demonstrate the effectiveness of the proposed method.