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Single-positive multi-label learning (SPMLL) is a typical weakly supervisedmulti-label learning problem, where each training example is annotated withonly one positive label. Existing SPMLL methods typically assign pseudo-labelsto unannotated labels with the assumption that prior probabilities of allclasses are identical. However, the class-prior of each category may differsignificantly in real-world scenarios, which makes the predictive model notperform as well as expected due to the unrealistic assumption on real-worldapplication. To alleviate this issue, a novel framework named {\proposed},i.e., Class-pRiors Induced Single-Positive multi-label learning, is proposed.Specifically, a class-priors estimator is introduced, which could estimate theclass-priors that are theoretically guaranteed to converge to the ground-truthclass-priors. In addition, based on the estimated class-priors, an unbiasedrisk estimator for classification is derived, and the corresponding riskminimizer could be guaranteed to approximately converge to the optimal riskminimizer on fully supervised data. Experimental results on ten MLL benchmarkdatasets demonstrate the effectiveness and superiority of our method overexisting SPMLL approaches.

Can Class-Priors Help Single-Positive Multi-Label Learning?