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In image classification, an image often has multiple labels, but it is expensive to obtain accurate label information. The main task of Partial Multilabel Learning (PML) is to learn in weakly supervised conditions where only a subset of the provided labels is correct and make correct predictions. Different from traditional multi-label learning, PML needs to train multi-label classification models in an imperfect environment, which belongs to weakly supervised learning. In order to reduce the false guidance of noise labels to the classifier and obtain correct prediction results, this paper proposes a novel PML method via Dual Subspace Collaboration (PML-DSC). Specifically, it first uses samples and labels to learn two common sub-spaces, then use fuzzy clustering to learn label class centers and calculate the confidence that the sample belongs to each label class center so that the first subspace can approximate the true label space to reduce the influence of noisy labels. The second subspace is then used to learn the potential semantic information, and the second subspace splits the classifier into two parts to improve the generalization ability of the model. Finally, the first subspace is used to guide the classifier learning, and the two sub-spaces work together to fully exploit the hidden information and improve the prediction accuracy of the model. Extensive experiments and analyses on both real-world and synthetic datasets demonstrate that the proposed PML-DSC is superior to the state-of-the-art methods.
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