  Single-positive multi-label learning (SPMLL) is a typical weakly supervised
multi-label learning problem, where each training example is annotated with
only one positive label. Existing SPMLL methods typically assign pseudo-labels
to unannotated labels with the assumption that prior probabilities of all
classes are identical. However, the class-prior of each category may differ
significantly in real-world scenarios, which makes the predictive model not
perform as well as expected due to the unrealistic assumption on real-world
application. 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 the
class-priors that are theoretically guaranteed to converge to the ground-truth
class-priors. In addition, based on the estimated class-priors, an unbiased
risk estimator for classification is derived, and the corresponding risk
minimizer could be guaranteed to approximately converge to the optimal risk
minimizer on fully supervised data. Experimental results on ten MLL benchmark
datasets demonstrate the effectiveness and superiority of our method over
existing SPMLL approaches.
