Neural Information Processing Systems http://nips.cc/

Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data.

Inlabel-noiselearning,thenoise transitionmatrix,bridgingtheclassposteriorfor noisy and clean data, has been widely exploited to learnstatistically consistent classifiers.

Experiments on twelve corrupted MLL datasets show the effectiveness of SMILEoverseveral existing SPMLL approaches.

Precisely annotating objects with multiple labels is costly and has become a critical bottleneck in real-world multi-label classification tasks. Instead, deciding the relative order of label pairs is obviously less laborious than collecting exact labels. However, the supervised information of pairwise relevance ordering is less informative than exact labels. It is thus an important challenge to effectively learn with such weak supervision. In this paper, we formalize this problem as a novel learning framework, called multi-label learning with pairwise relevance ordering (PRO). We show that the unbiased estimator of classification risk can be derived with a cost-sensitive loss only from PRO examples. Theoretically, we provide the estimation error bound for the proposed estimator and further prove that it is consistent with respective to the commonly used ranking loss. Empirical studies on multiple datasets and metrics validate the effectiveness of the proposed method.