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Partial multi-label learning (PML) tackles the problem that each example is assigned a candidate label set, of which only a subset is the ground-truth labels. By decomposing the problem using the first-order strategy, we found that PML is similar to the problem of learning with label noise. Motivated by this observation, we proposed a novel method, PML-CV, which tackles the PML problem with a cross-validation approach. To be specific, PML-CV enhances potentially correct labels by using cross-validation. And then use an example refining scheme to weaken the impact of noisy labels further. We also provide some theoretical analysis to explain the effectiveness of our proposed method. Finally, we conduct extensive experiments on different datasets to verify the effectiveness of our method. The experimental results verify that our method is comparable to current state-of-the-art methods.
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